Web_Scraping_NLP_Modeling.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "37139bf5",
   "metadata": {},
   "source": [
    "<hr style=\"border:2px solid black\">\n",
    "<b> Python Web Scraping & Natural Language Processing Modeling</b>\n",
    "\n",
    "<b> Individual Project </b>\n",
    "\n",
    "<b> Min Shi </b>\n",
    "    \n",
    "<hr style=\"border:2px solid black\">"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "49f68602",
   "metadata": {},
   "source": [
    "# Preparation"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8e001db3",
   "metadata": {},
   "source": [
    "## Import packages"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "5f83c77c",
   "metadata": {},
   "outputs": [],
   "source": [
    "import re\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from datetime import datetime\n",
    "import statsmodels.api as sm\n",
    "import os \n",
    "from os import getcwd\n",
    "import sys\n",
    "import csv\n",
    "import gensim\n",
    "import pickle\n",
    "import string\n",
    "from collections import Counter\n",
    "from sklearn.preprocessing import OneHotEncoder\n",
    "import nltk\n",
    "from nltk.corpus import stopwords\n",
    "from nltk.stem import PorterStemmer, WordNetLemmatizer\n",
    "from nltk.sentiment.vader import SentimentIntensityAnalyzer\n",
    "from nltk.classify.scikitlearn import SklearnClassifier\n",
    "from sklearn.decomposition import LatentDirichletAllocation\n",
    "from sklearn import model_selection\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.feature_extraction.text import CountVectorizer\n",
    "from sklearn.feature_extraction.text import TfidfVectorizer\n",
    "from sklearn.linear_model import LinearRegression,LogisticRegression, SGDClassifier \n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.naive_bayes import BernoulliNB, MultinomialNB\n",
    "from sklearn.svm import SVC\n",
    "from scipy.sparse import csr_matrix, hstack\n",
    "from sklearn.metrics import roc_auc_score\n",
    "from vaderSentiment.vaderSentiment import SentimentIntensityAnalyzer\n",
    "from textblob import TextBlob\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\", category=DeprecationWarning) \n",
    "\n",
    "if not sys.warnoptions:\n",
    "    import warnings\n",
    "    warnings.simplefilter(\"ignore\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "26b12712",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[nltk_data] Downloading package stopwords to /Users/min/nltk_data...\n",
      "[nltk_data]   Package stopwords is already up-to-date!\n",
      "[nltk_data] Downloading package wordnet to /Users/min/nltk_data...\n",
      "[nltk_data]   Package wordnet is already up-to-date!\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nltk.download('stopwords')\n",
    "nltk.download('wordnet')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d80c5796",
   "metadata": {},
   "source": [
    "## Specify the directory to use"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "99dedcde",
   "metadata": {},
   "outputs": [],
   "source": [
    "os.chdir(r'/Users/min/Desktop/2022 Fall Semester/BUAN 6342 NLP/Project/Proposals&Reports/Data_Analysis_in_Python')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4a23b17d",
   "metadata": {},
   "source": [
    "# Wall Street Journal (WSJ) News"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "3c965042",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 7645 entries, 0 to 7644\n",
      "Data columns (total 3 columns):\n",
      " #   Column     Non-Null Count  Dtype \n",
      "---  ------     --------------  ----- \n",
      " 0   NewsTitle  5869 non-null   object\n",
      " 1   Date       7645 non-null   object\n",
      " 2   NewsText   7645 non-null   object\n",
      "dtypes: object(3)\n",
      "memory usage: 179.3+ KB\n"
     ]
    }
   ],
   "source": [
    "df1 = pd.read_csv('WSJ_US_Trade.csv')\n",
    "df1.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "b4239f19",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>NewsTitle</th>\n",
       "      <th>Date</th>\n",
       "      <th>NewsText</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Free Trade Has Been a Boon for Energy Independ...</td>\n",
       "      <td>2018-01-01</td>\n",
       "      <td>As turbulent as Donald Trump's political care...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>France Looks to Deepen Trade Ties With Russia ...</td>\n",
       "      <td>2018-01-01</td>\n",
       "      <td>France is losing its appetite for trans-Atlant...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>A Cold War in the Arctic Circle; NATO plans a ...</td>\n",
       "      <td>2018-01-01</td>\n",
       "      <td>The Arctic is a region of tremendous strategic...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>World News: France Pursues Wider Trade Relatio...</td>\n",
       "      <td>2018-01-02</td>\n",
       "      <td>France is losing its appetite for trans-Atlant...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Free Trade Has Been a Boon for Energy Independ...</td>\n",
       "      <td>2018-01-02</td>\n",
       "      <td>As turbulent as Donald Trump's political caree...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                           NewsTitle        Date  \\\n",
       "0  Free Trade Has Been a Boon for Energy Independ...  2018-01-01   \n",
       "1  France Looks to Deepen Trade Ties With Russia ...  2018-01-01   \n",
       "2  A Cold War in the Arctic Circle; NATO plans a ...  2018-01-01   \n",
       "3  World News: France Pursues Wider Trade Relatio...  2018-01-02   \n",
       "4  Free Trade Has Been a Boon for Energy Independ...  2018-01-02   \n",
       "\n",
       "                                            NewsText  \n",
       "0   As turbulent as Donald Trump's political care...  \n",
       "1  France is losing its appetite for trans-Atlant...  \n",
       "2  The Arctic is a region of tremendous strategic...  \n",
       "3  France is losing its appetite for trans-Atlant...  \n",
       "4  As turbulent as Donald Trump's political caree...  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "d1411af7",
   "metadata": {},
   "outputs": [],
   "source": [
    "df1['Date'] = df1['Date'].astype('datetime64')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "b96d01f3",
   "metadata": {},
   "outputs": [],
   "source": [
    "def preprocess_text(text):\n",
    "    \n",
    "    stemmer = PorterStemmer()\n",
    "    \n",
    "    # Replace Numbers\n",
    "    text = re.sub(r'\\d+', '', text)\n",
    "    \n",
    "    # Lower Case\n",
    "    text = text.split(\" \") \n",
    "    text = [x.lower() for x in text]  #making all text data lowercase\n",
    "    \n",
    "    # Stop Words\n",
    "    stopwords_english = stopwords.words('english')\n",
    "    text_processed = []\n",
    "    \n",
    "    # Extra words\n",
    "    extra_words =['say','said','use','review','th','wall','street']\n",
    "\n",
    "    for word in text:\n",
    "         # remove stopwords and punctuation\n",
    "        if (word not in stopwords_english and word not in string.punctuation and word not in extra_words): \n",
    "            # Stemming\n",
    "            stem_word = stemmer.stem(word)\n",
    "            text_processed.append(stem_word)\n",
    "            \n",
    "    # Lemmatizing \n",
    "    lemmatizer = WordNetLemmatizer()\n",
    "  \n",
    "    text = [lemmatizer.lemmatize(x) for x in text_processed]\n",
    "           \n",
    "    return ' '.join(text)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "ff5edea7",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0       turbul donald trump' polit career thu far, alw...\n",
       "1       franc lose appetit trans-atlant trade.\\neconom...\n",
       "2       arctic region tremend strateg import global tr...\n",
       "3       franc lose appetit trans-atlant trade.\\neconom...\n",
       "4       turbul donald trump' polit career thu far, alw...\n",
       "                              ...                        \n",
       "7640    earli day parenthood, infant daughter colicki ...\n",
       "7641    rishi sunak prime minister. india britain. str...\n",
       "7642    chubb ltd. cb -.%\\ndecrease; red point triangl...\n",
       "7643    seoul—sk hynix inc., one world’ biggest chip m...\n",
       "7644    emperor penguin soon consid threaten speci end...\n",
       "Name: NewsText, Length: 7645, dtype: object"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1['NewsText'] = df1['NewsText'].apply(lambda x: preprocess_text(x))\n",
    "df1['NewsText']"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3c285b72",
   "metadata": {},
   "source": [
    "## Word Clouds"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "3d95aeb4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0       free trade boon energi independence; nafta del...\n",
       "1       franc look deepen trade tie russia china; brex...\n",
       "2       cold war arctic circle; nato plan new command ...\n",
       "3       world news: franc pursu wider trade relationsh...\n",
       "4                         free trade boon energi independ\n",
       "                              ...                        \n",
       "7634    hous democrat urg biden speak directli russia ...\n",
       "7637    intel ceo call new u.s. restrict chip export c...\n",
       "7642                  hurrican ian claim hit profit chubb\n",
       "7643                chip maker sk hynix slash capit spend\n",
       "7644             emperor penguin list threaten speci u.s.\n",
       "Name: NewsTitle, Length: 5869, dtype: object"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1_1 = df1['NewsTitle'].dropna()\n",
    "df1_1 = df1_1.astype('string')\n",
    "df1_1 = df1_1.apply(lambda x: preprocess_text(x))\n",
    "df1_1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "a307f0f9",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import the wordcloud library\n",
    "from wordcloud import WordCloud\n",
    "# Join the different processed titles together.\n",
    "title_long_string = ','.join(list(df1_1.values))\n",
    "text_long_string = ','.join(list(df1['NewsText'].values))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "0503d302",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<PIL.Image.Image image mode=RGB size=400x200 at 0x7F85C40D8E50>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# News Title Word Cloud\n",
    "# Create a WordCloud object\n",
    "wordcloud = WordCloud(background_color=\"white\", max_words=5000, contour_width=3, contour_color='steelblue')\n",
    "# Generate a word cloud\n",
    "wordcloud.generate(title_long_string)\n",
    "# Visualize the word cloud\n",
    "wordcloud.to_image()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "d6f3685b",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<PIL.Image.Image image mode=RGB size=400x200 at 0x7F85CD9CBA00>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# News Text Word Cloud\n",
    "# Create a WordCloud object\n",
    "wordcloud = WordCloud(background_color=\"white\", max_words=5000, contour_width=3, contour_color='steelblue')\n",
    "# Generate a word cloud\n",
    "wordcloud.generate(text_long_string)\n",
    "# Visualize the word cloud\n",
    "wordcloud.to_image()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b1e11f4f",
   "metadata": {},
   "source": [
    "**Based on the word clouds, we could tell that U.S. and China are the most frequent words appeared in the news articles related to U.S. trade, which means U.S.-China trade is one important part and a focus of U.S. trade. Besides, world, new, trade, stock, tariff, trump, and global are also frequent words appeared in the news article titles while company, say, said, trump, work and include are the relatively frequent words appeared in the news article text.**"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c350dc11",
   "metadata": {},
   "source": [
    "## Topic Modeling\n",
    "Topic modeling is a type of statistical modeling for discovering the abstract “topics” that occur in a collection of documents. Latent Dirichlet Allocation (LDA) is an example of topic model and is used to classify text in a document to a particular topic. It builds a topic per document model and words per topic model, modeled as Dirichlet distributions."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "026d3c0f",
   "metadata": {},
   "source": [
    "### For News Title"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "326571a7",
   "metadata": {},
   "outputs": [],
   "source": [
    "# the vectorizer object will be used to transform text to vector form\n",
    "vect = CountVectorizer(min_df = 20, max_df = 0.2, stop_words = 'english', token_pattern = '(?u)\\\\b\\\\w\\\\w\\\\w+\\\\b')\n",
    "\n",
    "# apply transformation\n",
    "tf = vect.fit_transform(df1_1)\n",
    "\n",
    "# tf_feature_names tells us what word each column in the matric represents\n",
    "tf_feature_names = vect.get_feature_names()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "d1b579a0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LatentDirichletAllocation(random_state=34)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LatentDirichletAllocation</label><div class=\"sk-toggleable__content\"><pre>LatentDirichletAllocation(random_state=34)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "LatentDirichletAllocation(random_state=34)"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "number_of_topics = 10\n",
    "\n",
    "Ldamodel = LatentDirichletAllocation(n_components=number_of_topics, random_state=34)\n",
    "\n",
    "Ldamodel.fit(tf)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "98cbd948",
   "metadata": {},
   "outputs": [],
   "source": [
    "def display_topics(Ldamodel, feature_names, no_top_words):\n",
    "    topic_dict = {}\n",
    "    for topic_idx, topic in enumerate(Ldamodel.components_):\n",
    "        topic_dict[\"Topic %d words\" % (topic_idx)]= ['{}'.format(feature_names[i])\n",
    "                        for i in topic.argsort()[:-no_top_words - 1:-1]]\n",
    "        topic_dict[\"Topic %d weights\" % (topic_idx)]= ['{:.1f}'.format(topic[i])\n",
    "                        for i in topic.argsort()[:-no_top_words - 1:-1]]\n",
    "    return pd.DataFrame(topic_dict)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "eff5d35e",
   "metadata": {},
   "source": [
    "#### LDA Topics\n",
    "Using ldamodel, find a list of the 10 topics and the most significant 10 words in each topic. This should be structured as a list of 10 tuples."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "3bfab278",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Topic 0 words</th>\n",
       "      <th>Topic 0 weights</th>\n",
       "      <th>Topic 1 words</th>\n",
       "      <th>Topic 1 weights</th>\n",
       "      <th>Topic 2 words</th>\n",
       "      <th>Topic 2 weights</th>\n",
       "      <th>Topic 3 words</th>\n",
       "      <th>Topic 3 weights</th>\n",
       "      <th>Topic 4 words</th>\n",
       "      <th>Topic 4 weights</th>\n",
       "      <th>Topic 5 words</th>\n",
       "      <th>Topic 5 weights</th>\n",
       "      <th>Topic 6 words</th>\n",
       "      <th>Topic 6 weights</th>\n",
       "      <th>Topic 7 words</th>\n",
       "      <th>Topic 7 weights</th>\n",
       "      <th>Topic 8 words</th>\n",
       "      <th>Topic 8 weights</th>\n",
       "      <th>Topic 9 words</th>\n",
       "      <th>Topic 9 weights</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>market</td>\n",
       "      <td>214.2</td>\n",
       "      <td>news</td>\n",
       "      <td>730.0</td>\n",
       "      <td>hous</td>\n",
       "      <td>79.1</td>\n",
       "      <td>trade</td>\n",
       "      <td>320.3</td>\n",
       "      <td>covid</td>\n",
       "      <td>268.7</td>\n",
       "      <td>new</td>\n",
       "      <td>311.4</td>\n",
       "      <td>price</td>\n",
       "      <td>207.0</td>\n",
       "      <td>review</td>\n",
       "      <td>204.1</td>\n",
       "      <td>trump</td>\n",
       "      <td>465.4</td>\n",
       "      <td>china</td>\n",
       "      <td>251.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>exchang</td>\n",
       "      <td>168.1</td>\n",
       "      <td>world</td>\n",
       "      <td>491.8</td>\n",
       "      <td>ahead</td>\n",
       "      <td>73.0</td>\n",
       "      <td>china</td>\n",
       "      <td>199.1</td>\n",
       "      <td>say</td>\n",
       "      <td>115.8</td>\n",
       "      <td>bank</td>\n",
       "      <td>79.4</td>\n",
       "      <td>oil</td>\n",
       "      <td>176.9</td>\n",
       "      <td>biden</td>\n",
       "      <td>157.0</td>\n",
       "      <td>tariff</td>\n",
       "      <td>381.5</td>\n",
       "      <td>chine</td>\n",
       "      <td>113.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>stock</td>\n",
       "      <td>92.5</td>\n",
       "      <td>china</td>\n",
       "      <td>194.2</td>\n",
       "      <td>economi</td>\n",
       "      <td>65.4</td>\n",
       "      <td>hit</td>\n",
       "      <td>142.0</td>\n",
       "      <td>vaccin</td>\n",
       "      <td>104.1</td>\n",
       "      <td>york</td>\n",
       "      <td>54.1</td>\n",
       "      <td>stock</td>\n",
       "      <td>169.7</td>\n",
       "      <td>russia</td>\n",
       "      <td>78.9</td>\n",
       "      <td>trade</td>\n",
       "      <td>283.7</td>\n",
       "      <td>news</td>\n",
       "      <td>100.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>amp</td>\n",
       "      <td>80.5</td>\n",
       "      <td>trade</td>\n",
       "      <td>123.3</td>\n",
       "      <td>white</td>\n",
       "      <td>50.1</td>\n",
       "      <td>talk</td>\n",
       "      <td>112.8</td>\n",
       "      <td>state</td>\n",
       "      <td>57.5</td>\n",
       "      <td>product</td>\n",
       "      <td>48.9</td>\n",
       "      <td>rise</td>\n",
       "      <td>130.8</td>\n",
       "      <td>court</td>\n",
       "      <td>74.1</td>\n",
       "      <td>china</td>\n",
       "      <td>205.6</td>\n",
       "      <td>busi</td>\n",
       "      <td>95.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>fall</td>\n",
       "      <td>77.9</td>\n",
       "      <td>growth</td>\n",
       "      <td>97.5</td>\n",
       "      <td>cut</td>\n",
       "      <td>48.7</td>\n",
       "      <td>deal</td>\n",
       "      <td>94.0</td>\n",
       "      <td>test</td>\n",
       "      <td>48.7</td>\n",
       "      <td>china</td>\n",
       "      <td>47.3</td>\n",
       "      <td>investor</td>\n",
       "      <td>118.1</td>\n",
       "      <td>rule</td>\n",
       "      <td>73.0</td>\n",
       "      <td>war</td>\n",
       "      <td>108.2</td>\n",
       "      <td>firm</td>\n",
       "      <td>92.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>china</td>\n",
       "      <td>68.9</td>\n",
       "      <td>busi</td>\n",
       "      <td>94.4</td>\n",
       "      <td>tax</td>\n",
       "      <td>48.1</td>\n",
       "      <td>report</td>\n",
       "      <td>79.3</td>\n",
       "      <td>elect</td>\n",
       "      <td>47.9</td>\n",
       "      <td>billion</td>\n",
       "      <td>41.9</td>\n",
       "      <td>fed</td>\n",
       "      <td>83.4</td>\n",
       "      <td>ukrain</td>\n",
       "      <td>67.3</td>\n",
       "      <td>steel</td>\n",
       "      <td>78.1</td>\n",
       "      <td>maker</td>\n",
       "      <td>74.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>week</td>\n",
       "      <td>57.8</td>\n",
       "      <td>beij</td>\n",
       "      <td>70.2</td>\n",
       "      <td>crisi</td>\n",
       "      <td>47.1</td>\n",
       "      <td>record</td>\n",
       "      <td>74.1</td>\n",
       "      <td>case</td>\n",
       "      <td>47.6</td>\n",
       "      <td>tesla</td>\n",
       "      <td>39.9</td>\n",
       "      <td>year</td>\n",
       "      <td>69.3</td>\n",
       "      <td>plan</td>\n",
       "      <td>59.1</td>\n",
       "      <td>presid</td>\n",
       "      <td>75.5</td>\n",
       "      <td>north</td>\n",
       "      <td>68.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>day</td>\n",
       "      <td>56.1</td>\n",
       "      <td>capit</td>\n",
       "      <td>70.1</td>\n",
       "      <td>set</td>\n",
       "      <td>43.0</td>\n",
       "      <td>tension</td>\n",
       "      <td>51.5</td>\n",
       "      <td>ceo</td>\n",
       "      <td>46.8</td>\n",
       "      <td>dollar</td>\n",
       "      <td>38.1</td>\n",
       "      <td>worri</td>\n",
       "      <td>66.3</td>\n",
       "      <td>pandem</td>\n",
       "      <td>53.0</td>\n",
       "      <td>import</td>\n",
       "      <td>57.9</td>\n",
       "      <td>korea</td>\n",
       "      <td>66.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>data</td>\n",
       "      <td>46.3</td>\n",
       "      <td>economi</td>\n",
       "      <td>62.7</td>\n",
       "      <td>oil</td>\n",
       "      <td>40.8</td>\n",
       "      <td>econom</td>\n",
       "      <td>48.0</td>\n",
       "      <td>biden</td>\n",
       "      <td>43.7</td>\n",
       "      <td>shift</td>\n",
       "      <td>37.8</td>\n",
       "      <td>global</td>\n",
       "      <td>64.1</td>\n",
       "      <td>foreign</td>\n",
       "      <td>47.4</td>\n",
       "      <td>tariffs</td>\n",
       "      <td>55.4</td>\n",
       "      <td>sanction</td>\n",
       "      <td>63.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>markets</td>\n",
       "      <td>43.1</td>\n",
       "      <td>global</td>\n",
       "      <td>56.8</td>\n",
       "      <td>say</td>\n",
       "      <td>38.7</td>\n",
       "      <td>year</td>\n",
       "      <td>46.8</td>\n",
       "      <td>trump</td>\n",
       "      <td>37.6</td>\n",
       "      <td>job</td>\n",
       "      <td>36.3</td>\n",
       "      <td>fear</td>\n",
       "      <td>61.7</td>\n",
       "      <td>work</td>\n",
       "      <td>46.1</td>\n",
       "      <td>good</td>\n",
       "      <td>49.5</td>\n",
       "      <td>sale</td>\n",
       "      <td>61.8</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Topic 0 words Topic 0 weights Topic 1 words Topic 1 weights Topic 2 words  \\\n",
       "0        market           214.2          news           730.0          hous   \n",
       "1       exchang           168.1         world           491.8         ahead   \n",
       "2         stock            92.5         china           194.2       economi   \n",
       "3           amp            80.5         trade           123.3         white   \n",
       "4          fall            77.9        growth            97.5           cut   \n",
       "5         china            68.9          busi            94.4           tax   \n",
       "6          week            57.8          beij            70.2         crisi   \n",
       "7           day            56.1         capit            70.1           set   \n",
       "8          data            46.3       economi            62.7           oil   \n",
       "9       markets            43.1        global            56.8           say   \n",
       "\n",
       "  Topic 2 weights Topic 3 words Topic 3 weights Topic 4 words Topic 4 weights  \\\n",
       "0            79.1         trade           320.3         covid           268.7   \n",
       "1            73.0         china           199.1           say           115.8   \n",
       "2            65.4           hit           142.0        vaccin           104.1   \n",
       "3            50.1          talk           112.8         state            57.5   \n",
       "4            48.7          deal            94.0          test            48.7   \n",
       "5            48.1        report            79.3         elect            47.9   \n",
       "6            47.1        record            74.1          case            47.6   \n",
       "7            43.0       tension            51.5           ceo            46.8   \n",
       "8            40.8        econom            48.0         biden            43.7   \n",
       "9            38.7          year            46.8         trump            37.6   \n",
       "\n",
       "  Topic 5 words Topic 5 weights Topic 6 words Topic 6 weights Topic 7 words  \\\n",
       "0           new           311.4         price           207.0        review   \n",
       "1          bank            79.4           oil           176.9         biden   \n",
       "2          york            54.1         stock           169.7        russia   \n",
       "3       product            48.9          rise           130.8         court   \n",
       "4         china            47.3      investor           118.1          rule   \n",
       "5       billion            41.9           fed            83.4        ukrain   \n",
       "6         tesla            39.9          year            69.3          plan   \n",
       "7        dollar            38.1         worri            66.3        pandem   \n",
       "8         shift            37.8        global            64.1       foreign   \n",
       "9           job            36.3          fear            61.7          work   \n",
       "\n",
       "  Topic 7 weights Topic 8 words Topic 8 weights Topic 9 words Topic 9 weights  \n",
       "0           204.1         trump           465.4         china           251.8  \n",
       "1           157.0        tariff           381.5         chine           113.2  \n",
       "2            78.9         trade           283.7          news           100.3  \n",
       "3            74.1         china           205.6          busi            95.7  \n",
       "4            73.0           war           108.2          firm            92.7  \n",
       "5            67.3         steel            78.1         maker            74.9  \n",
       "6            59.1        presid            75.5         north            68.1  \n",
       "7            53.0        import            57.9         korea            66.1  \n",
       "8            47.4       tariffs            55.4      sanction            63.4  \n",
       "9            46.1          good            49.5          sale            61.8  "
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "no_top_words = 10\n",
    "df_topics = display_topics(Ldamodel, tf_feature_names, no_top_words)\n",
    "df_topics"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fd05a7a1",
   "metadata": {},
   "source": [
    "#### Topic Names\n",
    "From the list of the following given topics, assign topic names to the topics you found. If none of these names best matches the topics you found, create a new 1-3 word \"title\" for the topic."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "7cef05d2",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_topics = df_topics.rename(columns = {'Topic 0 words': 'Topic 1 Market', \n",
    "                                       'Topic 1 words': 'Topic 2 Global Trade', \n",
    "                                       'Topic 2 words': 'Topic 3 Economy',\n",
    "                                       'Topic 3 words': 'Topic 4 Trade & Deal',\n",
    "                                       'Topic 4 words': 'Topic 5 Covid & Vaccine',\n",
    "                                       'Topic 5 words': 'Topic 6 Products',\n",
    "                                       'Topic 6 words': 'Topic 7 Invest',\n",
    "                                       'Topic 7 words': 'Topic 8 Russia & Ukraine',\n",
    "                                       'Topic 8 words': 'Topic 9 Trade War',\n",
    "                                       'Topic 9 words': 'Topic 10 China'\n",
    "                                       })\n",
    "df_topics = df_topics.drop(columns = ['Topic 0 weights', 'Topic 1 weights', 'Topic 2 weights',\n",
    "                                      'Topic 3 weights', 'Topic 4 weights', 'Topic 5 weights',\n",
    "                                      'Topic 6 weights', 'Topic 7 weights', 'Topic 8 weights',\n",
    "                                      'Topic 9 weights'], axis = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "b63c0fdb",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
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       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Topic 1 Market</th>\n",
       "      <th>Topic 2 Global Trade</th>\n",
       "      <th>Topic 3 Economy</th>\n",
       "      <th>Topic 4 Trade &amp; Deal</th>\n",
       "      <th>Topic 5 Covid &amp; Vaccine</th>\n",
       "      <th>Topic 6 Products</th>\n",
       "      <th>Topic 7 Invest</th>\n",
       "      <th>Topic 8 Russia &amp; Ukraine</th>\n",
       "      <th>Topic 9 Trade War</th>\n",
       "      <th>Topic 10 China</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>market</td>\n",
       "      <td>news</td>\n",
       "      <td>hous</td>\n",
       "      <td>trade</td>\n",
       "      <td>covid</td>\n",
       "      <td>new</td>\n",
       "      <td>price</td>\n",
       "      <td>review</td>\n",
       "      <td>trump</td>\n",
       "      <td>china</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>exchang</td>\n",
       "      <td>world</td>\n",
       "      <td>ahead</td>\n",
       "      <td>china</td>\n",
       "      <td>say</td>\n",
       "      <td>bank</td>\n",
       "      <td>oil</td>\n",
       "      <td>biden</td>\n",
       "      <td>tariff</td>\n",
       "      <td>chine</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>stock</td>\n",
       "      <td>china</td>\n",
       "      <td>economi</td>\n",
       "      <td>hit</td>\n",
       "      <td>vaccin</td>\n",
       "      <td>york</td>\n",
       "      <td>stock</td>\n",
       "      <td>russia</td>\n",
       "      <td>trade</td>\n",
       "      <td>news</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>amp</td>\n",
       "      <td>trade</td>\n",
       "      <td>white</td>\n",
       "      <td>talk</td>\n",
       "      <td>state</td>\n",
       "      <td>product</td>\n",
       "      <td>rise</td>\n",
       "      <td>court</td>\n",
       "      <td>china</td>\n",
       "      <td>busi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>fall</td>\n",
       "      <td>growth</td>\n",
       "      <td>cut</td>\n",
       "      <td>deal</td>\n",
       "      <td>test</td>\n",
       "      <td>china</td>\n",
       "      <td>investor</td>\n",
       "      <td>rule</td>\n",
       "      <td>war</td>\n",
       "      <td>firm</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>china</td>\n",
       "      <td>busi</td>\n",
       "      <td>tax</td>\n",
       "      <td>report</td>\n",
       "      <td>elect</td>\n",
       "      <td>billion</td>\n",
       "      <td>fed</td>\n",
       "      <td>ukrain</td>\n",
       "      <td>steel</td>\n",
       "      <td>maker</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>week</td>\n",
       "      <td>beij</td>\n",
       "      <td>crisi</td>\n",
       "      <td>record</td>\n",
       "      <td>case</td>\n",
       "      <td>tesla</td>\n",
       "      <td>year</td>\n",
       "      <td>plan</td>\n",
       "      <td>presid</td>\n",
       "      <td>north</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>day</td>\n",
       "      <td>capit</td>\n",
       "      <td>set</td>\n",
       "      <td>tension</td>\n",
       "      <td>ceo</td>\n",
       "      <td>dollar</td>\n",
       "      <td>worri</td>\n",
       "      <td>pandem</td>\n",
       "      <td>import</td>\n",
       "      <td>korea</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>data</td>\n",
       "      <td>economi</td>\n",
       "      <td>oil</td>\n",
       "      <td>econom</td>\n",
       "      <td>biden</td>\n",
       "      <td>shift</td>\n",
       "      <td>global</td>\n",
       "      <td>foreign</td>\n",
       "      <td>tariffs</td>\n",
       "      <td>sanction</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>markets</td>\n",
       "      <td>global</td>\n",
       "      <td>say</td>\n",
       "      <td>year</td>\n",
       "      <td>trump</td>\n",
       "      <td>job</td>\n",
       "      <td>fear</td>\n",
       "      <td>work</td>\n",
       "      <td>good</td>\n",
       "      <td>sale</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Topic 1 Market Topic 2 Global Trade Topic 3 Economy Topic 4 Trade & Deal  \\\n",
       "0         market                 news            hous                trade   \n",
       "1        exchang                world           ahead                china   \n",
       "2          stock                china         economi                  hit   \n",
       "3            amp                trade           white                 talk   \n",
       "4           fall               growth             cut                 deal   \n",
       "5          china                 busi             tax               report   \n",
       "6           week                 beij           crisi               record   \n",
       "7            day                capit             set              tension   \n",
       "8           data              economi             oil               econom   \n",
       "9        markets               global             say                 year   \n",
       "\n",
       "  Topic 5 Covid & Vaccine Topic 6 Products Topic 7 Invest  \\\n",
       "0                   covid              new          price   \n",
       "1                     say             bank            oil   \n",
       "2                  vaccin             york          stock   \n",
       "3                   state          product           rise   \n",
       "4                    test            china       investor   \n",
       "5                   elect          billion            fed   \n",
       "6                    case            tesla           year   \n",
       "7                     ceo           dollar          worri   \n",
       "8                   biden            shift         global   \n",
       "9                   trump              job           fear   \n",
       "\n",
       "  Topic 8 Russia & Ukraine Topic 9 Trade War Topic 10 China  \n",
       "0                   review             trump          china  \n",
       "1                    biden            tariff          chine  \n",
       "2                   russia             trade           news  \n",
       "3                    court             china           busi  \n",
       "4                     rule               war           firm  \n",
       "5                   ukrain             steel          maker  \n",
       "6                     plan            presid          north  \n",
       "7                   pandem            import          korea  \n",
       "8                  foreign           tariffs       sanction  \n",
       "9                     work              good           sale  "
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_topics.to_csv('News_Title_Topics.csv')\n",
    "df_topics"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9ee5bd9f",
   "metadata": {},
   "source": [
    "### For News Text"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "a49597df",
   "metadata": {},
   "outputs": [],
   "source": [
    "# the vectorizer object will be used to transform text to vector form\n",
    "vect = CountVectorizer(min_df = 20, max_df = 0.2, stop_words = 'english', token_pattern = '(?u)\\\\b\\\\w\\\\w+\\\\b')\n",
    "\n",
    "# apply transformation\n",
    "tf = vect.fit_transform(df1['NewsText'])\n",
    "\n",
    "# tf_feature_names tells us what word each column in the matric represents\n",
    "tf_feature_names = vect.get_feature_names()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "76dbfa79",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-2 {color: black;background-color: white;}#sk-container-id-2 pre{padding: 0;}#sk-container-id-2 div.sk-toggleable {background-color: white;}#sk-container-id-2 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-2 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-2 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-2 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-2 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-2 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-2 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-2 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-2 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-2 div.sk-item {position: relative;z-index: 1;}#sk-container-id-2 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-2 div.sk-item::before, #sk-container-id-2 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-2 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-2 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-2 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-2 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-2 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-2 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-2 div.sk-label-container {text-align: center;}#sk-container-id-2 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-2 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LatentDirichletAllocation(random_state=34)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" checked><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LatentDirichletAllocation</label><div class=\"sk-toggleable__content\"><pre>LatentDirichletAllocation(random_state=34)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "LatentDirichletAllocation(random_state=34)"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "number_of_topics = 10\n",
    "\n",
    "Ldamodel = LatentDirichletAllocation(n_components=number_of_topics, random_state=34)\n",
    "\n",
    "Ldamodel.fit(tf)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "45fae832",
   "metadata": {},
   "outputs": [],
   "source": [
    "def display_topics(Ldamodel, feature_names, no_top_words):\n",
    "    topic_dict = {}\n",
    "    for topic_idx, topic in enumerate(Ldamodel.components_):\n",
    "        topic_dict[\"Topic %d words\" % (topic_idx)]= ['{}'.format(feature_names[i])\n",
    "                        for i in topic.argsort()[:-no_top_words - 1:-1]]\n",
    "        topic_dict[\"Topic %d weights\" % (topic_idx)]= ['{:.1f}'.format(topic[i])\n",
    "                        for i in topic.argsort()[:-no_top_words - 1:-1]]\n",
    "    return pd.DataFrame(topic_dict)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f582847c",
   "metadata": {},
   "source": [
    "#### LDA Topics\n",
    "Using ldamodel, find a list of the 10 topics and the most significant 10 words in each topic. This should be structured as a list of 10 tuples."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "5c7b873f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Topic 0 words</th>\n",
       "      <th>Topic 0 weights</th>\n",
       "      <th>Topic 1 words</th>\n",
       "      <th>Topic 1 weights</th>\n",
       "      <th>Topic 2 words</th>\n",
       "      <th>Topic 2 weights</th>\n",
       "      <th>Topic 3 words</th>\n",
       "      <th>Topic 3 weights</th>\n",
       "      <th>Topic 4 words</th>\n",
       "      <th>Topic 4 weights</th>\n",
       "      <th>Topic 5 words</th>\n",
       "      <th>Topic 5 weights</th>\n",
       "      <th>Topic 6 words</th>\n",
       "      <th>Topic 6 weights</th>\n",
       "      <th>Topic 7 words</th>\n",
       "      <th>Topic 7 weights</th>\n",
       "      <th>Topic 8 words</th>\n",
       "      <th>Topic 8 weights</th>\n",
       "      <th>Topic 9 words</th>\n",
       "      <th>Topic 9 weights</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>company</td>\n",
       "      <td>1304.1</td>\n",
       "      <td>eu</td>\n",
       "      <td>1980.1</td>\n",
       "      <td>beij</td>\n",
       "      <td>2103.0</td>\n",
       "      <td>democrat</td>\n",
       "      <td>4094.9</td>\n",
       "      <td>court</td>\n",
       "      <td>2887.3</td>\n",
       "      <td>stock</td>\n",
       "      <td>4017.2</td>\n",
       "      <td>vaccin</td>\n",
       "      <td>3919.5</td>\n",
       "      <td>manufactur</td>\n",
       "      <td>2138.7</td>\n",
       "      <td>russia</td>\n",
       "      <td>5430.5</td>\n",
       "      <td>life</td>\n",
       "      <td>1591.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>tech</td>\n",
       "      <td>1157.7</td>\n",
       "      <td>north</td>\n",
       "      <td>1960.9</td>\n",
       "      <td>huawei</td>\n",
       "      <td>1821.1</td>\n",
       "      <td>republican</td>\n",
       "      <td>2987.0</td>\n",
       "      <td>school</td>\n",
       "      <td>1910.8</td>\n",
       "      <td>fed</td>\n",
       "      <td>2725.2</td>\n",
       "      <td>covid</td>\n",
       "      <td>3356.7</td>\n",
       "      <td>car</td>\n",
       "      <td>1924.4</td>\n",
       "      <td>oil</td>\n",
       "      <td>4879.3</td>\n",
       "      <td>book</td>\n",
       "      <td>1317.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>appl</td>\n",
       "      <td>1145.8</td>\n",
       "      <td>steel</td>\n",
       "      <td>1782.6</td>\n",
       "      <td>xi</td>\n",
       "      <td>1729.9</td>\n",
       "      <td>vote</td>\n",
       "      <td>2378.0</td>\n",
       "      <td>justic</td>\n",
       "      <td>1605.0</td>\n",
       "      <td>dollar</td>\n",
       "      <td>2621.7</td>\n",
       "      <td>health</td>\n",
       "      <td>2508.7</td>\n",
       "      <td>maker</td>\n",
       "      <td>1509.9</td>\n",
       "      <td>russian</td>\n",
       "      <td>4000.6</td>\n",
       "      <td>says</td>\n",
       "      <td>1200.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>employe</td>\n",
       "      <td>1141.4</td>\n",
       "      <td>mexico</td>\n",
       "      <td>1601.9</td>\n",
       "      <td>beijing</td>\n",
       "      <td>1190.6</td>\n",
       "      <td>elect</td>\n",
       "      <td>2318.3</td>\n",
       "      <td>student</td>\n",
       "      <td>1273.9</td>\n",
       "      <td>inflat</td>\n",
       "      <td>2125.4</td>\n",
       "      <td>test</td>\n",
       "      <td>1570.6</td>\n",
       "      <td>sale</td>\n",
       "      <td>1371.2</td>\n",
       "      <td>energi</td>\n",
       "      <td>2059.0</td>\n",
       "      <td>live</td>\n",
       "      <td>1146.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>job</td>\n",
       "      <td>1108.3</td>\n",
       "      <td>negoti</td>\n",
       "      <td>1596.1</td>\n",
       "      <td>iran</td>\n",
       "      <td>1187.8</td>\n",
       "      <td>tax</td>\n",
       "      <td>2234.7</td>\n",
       "      <td>rule</td>\n",
       "      <td>1132.5</td>\n",
       "      <td>index</td>\n",
       "      <td>2108.9</td>\n",
       "      <td>dr</td>\n",
       "      <td>1093.4</td>\n",
       "      <td>plant</td>\n",
       "      <td>1340.9</td>\n",
       "      <td>ukrain</td>\n",
       "      <td>1983.0</td>\n",
       "      <td>old</td>\n",
       "      <td>1135.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>app</td>\n",
       "      <td>1005.1</td>\n",
       "      <td>agreement</td>\n",
       "      <td>1548.0</td>\n",
       "      <td>india</td>\n",
       "      <td>1167.3</td>\n",
       "      <td>senat</td>\n",
       "      <td>1940.8</td>\n",
       "      <td>judg</td>\n",
       "      <td>996.1</td>\n",
       "      <td>rose</td>\n",
       "      <td>1862.0</td>\n",
       "      <td>viru</td>\n",
       "      <td>960.2</td>\n",
       "      <td>electr</td>\n",
       "      <td>1187.8</td>\n",
       "      <td>ga</td>\n",
       "      <td>1940.7</td>\n",
       "      <td>play</td>\n",
       "      <td>1083.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>pay</td>\n",
       "      <td>1000.6</td>\n",
       "      <td>korea</td>\n",
       "      <td>1441.5</td>\n",
       "      <td>hong</td>\n",
       "      <td>1134.2</td>\n",
       "      <td>congress</td>\n",
       "      <td>1372.2</td>\n",
       "      <td>board</td>\n",
       "      <td>964.5</td>\n",
       "      <td>fell</td>\n",
       "      <td>1857.4</td>\n",
       "      <td>drug</td>\n",
       "      <td>886.6</td>\n",
       "      <td>auto</td>\n",
       "      <td>1164.0</td>\n",
       "      <td>ukraine</td>\n",
       "      <td>1849.1</td>\n",
       "      <td>feel</td>\n",
       "      <td>901.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>worker</td>\n",
       "      <td>995.0</td>\n",
       "      <td>canada</td>\n",
       "      <td>1217.5</td>\n",
       "      <td>militari</td>\n",
       "      <td>1127.4</td>\n",
       "      <td>parti</td>\n",
       "      <td>1255.3</td>\n",
       "      <td>investig</td>\n",
       "      <td>883.0</td>\n",
       "      <td>quarter</td>\n",
       "      <td>1824.1</td>\n",
       "      <td>dose</td>\n",
       "      <td>872.4</td>\n",
       "      <td>vehicl</td>\n",
       "      <td>1101.1</td>\n",
       "      <td>putin</td>\n",
       "      <td>1500.0</td>\n",
       "      <td>art</td>\n",
       "      <td>835.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>amazon</td>\n",
       "      <td>987.9</td>\n",
       "      <td>tariffs</td>\n",
       "      <td>1191.1</td>\n",
       "      <td>kong</td>\n",
       "      <td>1102.1</td>\n",
       "      <td>sen</td>\n",
       "      <td>1215.6</td>\n",
       "      <td>legal</td>\n",
       "      <td>812.9</td>\n",
       "      <td>economist</td>\n",
       "      <td>1810.4</td>\n",
       "      <td>hospit</td>\n",
       "      <td>810.9</td>\n",
       "      <td>factori</td>\n",
       "      <td>1076.2</td>\n",
       "      <td>ukrainian</td>\n",
       "      <td>1442.1</td>\n",
       "      <td>famili</td>\n",
       "      <td>834.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>servic</td>\n",
       "      <td>957.1</td>\n",
       "      <td>impos</td>\n",
       "      <td>1187.2</td>\n",
       "      <td>taiwan</td>\n",
       "      <td>844.3</td>\n",
       "      <td>voter</td>\n",
       "      <td>1169.6</td>\n",
       "      <td>depart</td>\n",
       "      <td>788.0</td>\n",
       "      <td>central</td>\n",
       "      <td>1806.2</td>\n",
       "      <td>coronaviru</td>\n",
       "      <td>798.1</td>\n",
       "      <td>ship</td>\n",
       "      <td>1000.7</td>\n",
       "      <td>sanction</td>\n",
       "      <td>1434.5</td>\n",
       "      <td>thought</td>\n",
       "      <td>794.4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Topic 0 words Topic 0 weights Topic 1 words Topic 1 weights Topic 2 words  \\\n",
       "0       company          1304.1            eu          1980.1          beij   \n",
       "1          tech          1157.7         north          1960.9        huawei   \n",
       "2          appl          1145.8         steel          1782.6            xi   \n",
       "3       employe          1141.4        mexico          1601.9       beijing   \n",
       "4           job          1108.3        negoti          1596.1          iran   \n",
       "5           app          1005.1     agreement          1548.0         india   \n",
       "6           pay          1000.6         korea          1441.5          hong   \n",
       "7        worker           995.0        canada          1217.5      militari   \n",
       "8        amazon           987.9       tariffs          1191.1          kong   \n",
       "9        servic           957.1         impos          1187.2        taiwan   \n",
       "\n",
       "  Topic 2 weights Topic 3 words Topic 3 weights Topic 4 words Topic 4 weights  \\\n",
       "0          2103.0      democrat          4094.9         court          2887.3   \n",
       "1          1821.1    republican          2987.0        school          1910.8   \n",
       "2          1729.9          vote          2378.0        justic          1605.0   \n",
       "3          1190.6         elect          2318.3       student          1273.9   \n",
       "4          1187.8           tax          2234.7          rule          1132.5   \n",
       "5          1167.3         senat          1940.8          judg           996.1   \n",
       "6          1134.2      congress          1372.2         board           964.5   \n",
       "7          1127.4         parti          1255.3      investig           883.0   \n",
       "8          1102.1           sen          1215.6         legal           812.9   \n",
       "9           844.3         voter          1169.6        depart           788.0   \n",
       "\n",
       "  Topic 5 words Topic 5 weights Topic 6 words Topic 6 weights Topic 7 words  \\\n",
       "0         stock          4017.2        vaccin          3919.5    manufactur   \n",
       "1           fed          2725.2         covid          3356.7           car   \n",
       "2        dollar          2621.7        health          2508.7         maker   \n",
       "3        inflat          2125.4          test          1570.6          sale   \n",
       "4         index          2108.9            dr          1093.4         plant   \n",
       "5          rose          1862.0          viru           960.2        electr   \n",
       "6          fell          1857.4          drug           886.6          auto   \n",
       "7       quarter          1824.1          dose           872.4        vehicl   \n",
       "8     economist          1810.4        hospit           810.9       factori   \n",
       "9       central          1806.2    coronaviru           798.1          ship   \n",
       "\n",
       "  Topic 7 weights Topic 8 words Topic 8 weights Topic 9 words Topic 9 weights  \n",
       "0          2138.7        russia          5430.5          life          1591.2  \n",
       "1          1924.4           oil          4879.3          book          1317.9  \n",
       "2          1509.9       russian          4000.6          says          1200.2  \n",
       "3          1371.2        energi          2059.0          live          1146.3  \n",
       "4          1340.9        ukrain          1983.0           old          1135.6  \n",
       "5          1187.8            ga          1940.7          play          1083.3  \n",
       "6          1164.0       ukraine          1849.1          feel           901.8  \n",
       "7          1101.1         putin          1500.0           art           835.5  \n",
       "8          1076.2     ukrainian          1442.1        famili           834.2  \n",
       "9          1000.7      sanction          1434.5       thought           794.4  "
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "no_top_words = 10\n",
    "df_topics2 = display_topics(Ldamodel, tf_feature_names, no_top_words)\n",
    "df_topics2"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "15824816",
   "metadata": {},
   "source": [
    "#### Topic Names\n",
    "From the list of the following given topics, assign topic names to the topics you found. If none of these names best matches the topics you found, create a new 1-3 word \"title\" for the topic."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "fbe2ad87",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_topics2 = df_topics2.rename(columns = {'Topic 0 words': 'Topic 1 Tech Company', \n",
    "                                       'Topic 1 words': 'Topic 2 Global Trade', \n",
    "                                       'Topic 2 words': 'Topic 3 International Relations',\n",
    "                                       'Topic 3 words': 'Topic 4 Politics & Election',\n",
    "                                       'Topic 4 words': 'Topic 5 School & College',\n",
    "                                       'Topic 5 words': 'Topic 6 Stock & Inflation',\n",
    "                                       'Topic 6 words': 'Topic 7 Covid & Vaccine',\n",
    "                                       'Topic 7 words': 'Topic 8 Russia & Ukraine',\n",
    "                                       'Topic 8 words': 'Topic 9 Manufacture',\n",
    "                                       'Topic 9 words': 'Topic 10 Life & Family'\n",
    "                                       })\n",
    "df_topics2 = df_topics2.drop(columns = ['Topic 0 weights', 'Topic 1 weights', 'Topic 2 weights',\n",
    "                                      'Topic 3 weights', 'Topic 4 weights', 'Topic 5 weights',\n",
    "                                      'Topic 6 weights', 'Topic 7 weights', 'Topic 8 weights',\n",
    "                                      'Topic 9 weights'], axis = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "527f2efb",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Topic 1 Tech Company</th>\n",
       "      <th>Topic 2 Global Trade</th>\n",
       "      <th>Topic 3 International Relations</th>\n",
       "      <th>Topic 4 Politics &amp; Election</th>\n",
       "      <th>Topic 5 School &amp; College</th>\n",
       "      <th>Topic 6 Stock &amp; Inflation</th>\n",
       "      <th>Topic 7 Covid &amp; Vaccine</th>\n",
       "      <th>Topic 8 Russia &amp; Ukraine</th>\n",
       "      <th>Topic 9 Manufacture</th>\n",
       "      <th>Topic 10 Life &amp; Family</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>company</td>\n",
       "      <td>eu</td>\n",
       "      <td>beij</td>\n",
       "      <td>democrat</td>\n",
       "      <td>court</td>\n",
       "      <td>stock</td>\n",
       "      <td>vaccin</td>\n",
       "      <td>manufactur</td>\n",
       "      <td>russia</td>\n",
       "      <td>life</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>tech</td>\n",
       "      <td>north</td>\n",
       "      <td>huawei</td>\n",
       "      <td>republican</td>\n",
       "      <td>school</td>\n",
       "      <td>fed</td>\n",
       "      <td>covid</td>\n",
       "      <td>car</td>\n",
       "      <td>oil</td>\n",
       "      <td>book</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>appl</td>\n",
       "      <td>steel</td>\n",
       "      <td>xi</td>\n",
       "      <td>vote</td>\n",
       "      <td>justic</td>\n",
       "      <td>dollar</td>\n",
       "      <td>health</td>\n",
       "      <td>maker</td>\n",
       "      <td>russian</td>\n",
       "      <td>says</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>employe</td>\n",
       "      <td>mexico</td>\n",
       "      <td>beijing</td>\n",
       "      <td>elect</td>\n",
       "      <td>student</td>\n",
       "      <td>inflat</td>\n",
       "      <td>test</td>\n",
       "      <td>sale</td>\n",
       "      <td>energi</td>\n",
       "      <td>live</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>job</td>\n",
       "      <td>negoti</td>\n",
       "      <td>iran</td>\n",
       "      <td>tax</td>\n",
       "      <td>rule</td>\n",
       "      <td>index</td>\n",
       "      <td>dr</td>\n",
       "      <td>plant</td>\n",
       "      <td>ukrain</td>\n",
       "      <td>old</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>app</td>\n",
       "      <td>agreement</td>\n",
       "      <td>india</td>\n",
       "      <td>senat</td>\n",
       "      <td>judg</td>\n",
       "      <td>rose</td>\n",
       "      <td>viru</td>\n",
       "      <td>electr</td>\n",
       "      <td>ga</td>\n",
       "      <td>play</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>pay</td>\n",
       "      <td>korea</td>\n",
       "      <td>hong</td>\n",
       "      <td>congress</td>\n",
       "      <td>board</td>\n",
       "      <td>fell</td>\n",
       "      <td>drug</td>\n",
       "      <td>auto</td>\n",
       "      <td>ukraine</td>\n",
       "      <td>feel</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>worker</td>\n",
       "      <td>canada</td>\n",
       "      <td>militari</td>\n",
       "      <td>parti</td>\n",
       "      <td>investig</td>\n",
       "      <td>quarter</td>\n",
       "      <td>dose</td>\n",
       "      <td>vehicl</td>\n",
       "      <td>putin</td>\n",
       "      <td>art</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>amazon</td>\n",
       "      <td>tariffs</td>\n",
       "      <td>kong</td>\n",
       "      <td>sen</td>\n",
       "      <td>legal</td>\n",
       "      <td>economist</td>\n",
       "      <td>hospit</td>\n",
       "      <td>factori</td>\n",
       "      <td>ukrainian</td>\n",
       "      <td>famili</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>servic</td>\n",
       "      <td>impos</td>\n",
       "      <td>taiwan</td>\n",
       "      <td>voter</td>\n",
       "      <td>depart</td>\n",
       "      <td>central</td>\n",
       "      <td>coronaviru</td>\n",
       "      <td>ship</td>\n",
       "      <td>sanction</td>\n",
       "      <td>thought</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Topic 1 Tech Company Topic 2 Global Trade Topic 3 International Relations  \\\n",
       "0              company                   eu                            beij   \n",
       "1                 tech                north                          huawei   \n",
       "2                 appl                steel                              xi   \n",
       "3              employe               mexico                         beijing   \n",
       "4                  job               negoti                            iran   \n",
       "5                  app            agreement                           india   \n",
       "6                  pay                korea                            hong   \n",
       "7               worker               canada                        militari   \n",
       "8               amazon              tariffs                            kong   \n",
       "9               servic                impos                          taiwan   \n",
       "\n",
       "  Topic 4 Politics & Election Topic 5 School & College  \\\n",
       "0                    democrat                    court   \n",
       "1                  republican                   school   \n",
       "2                        vote                   justic   \n",
       "3                       elect                  student   \n",
       "4                         tax                     rule   \n",
       "5                       senat                     judg   \n",
       "6                    congress                    board   \n",
       "7                       parti                 investig   \n",
       "8                         sen                    legal   \n",
       "9                       voter                   depart   \n",
       "\n",
       "  Topic 6 Stock & Inflation Topic 7 Covid & Vaccine Topic 8 Russia & Ukraine  \\\n",
       "0                     stock                  vaccin               manufactur   \n",
       "1                       fed                   covid                      car   \n",
       "2                    dollar                  health                    maker   \n",
       "3                    inflat                    test                     sale   \n",
       "4                     index                      dr                    plant   \n",
       "5                      rose                    viru                   electr   \n",
       "6                      fell                    drug                     auto   \n",
       "7                   quarter                    dose                   vehicl   \n",
       "8                 economist                  hospit                  factori   \n",
       "9                   central              coronaviru                     ship   \n",
       "\n",
       "  Topic 9 Manufacture Topic 10 Life & Family  \n",
       "0              russia                   life  \n",
       "1                 oil                   book  \n",
       "2             russian                   says  \n",
       "3              energi                   live  \n",
       "4              ukrain                    old  \n",
       "5                  ga                   play  \n",
       "6             ukraine                   feel  \n",
       "7               putin                    art  \n",
       "8           ukrainian                 famili  \n",
       "9            sanction                thought  "
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_topics2.to_csv('News_Title_Topics2.csv')\n",
    "df_topics2"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ec7da032",
   "metadata": {},
   "source": [
    "## Sentiment Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "9baa0212",
   "metadata": {},
   "outputs": [],
   "source": [
    "#generating the VADER sentiment scores\n",
    "sid = SentimentIntensityAnalyzer()\n",
    "df1['Newspaper_Sentiment_VADER'] = df1['NewsText'].apply(lambda x : sid.polarity_scores(x))\n",
    "#separating the VADER negativity, positivity, neutrality and compound scores in the full dataset\n",
    "df1['VADER_Newspaper_Negative'] = df1['Newspaper_Sentiment_VADER'].apply(lambda score_dict: score_dict['neg'])\n",
    "df1['VADER_Newspaper_Positive'] = df1['Newspaper_Sentiment_VADER'].apply(lambda score_dict: score_dict['pos'])\n",
    "df1['VADER_Newspaper_Neutral'] = df1['Newspaper_Sentiment_VADER'].apply(lambda score_dict: score_dict['neu'])\n",
    "df1['VADER_Newspaper_Compound'] = df1['Newspaper_Sentiment_VADER'].apply(lambda score_dict: score_dict['compound'])\n",
    "\n",
    "#generating the TextBlob scores\n",
    "df1['TextBlob_Newspaper_Sentiment'] = df1['NewsText'].apply(lambda x: TextBlob(x).sentiment)\n",
    "#separating the two TextBlob polarity and subjectivity scores for Paragraph and Headline \n",
    "df1['TextBlob_Newspaper_Sentiment_Polarity'], df1['TextBlob_Newspaper_Sentiment_Subjectivity'] = df1.TextBlob_Newspaper_Sentiment.str"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "1a34c8a0",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "<style scoped>\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
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       "\n",
       "    .dataframe thead th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>NewsTitle</th>\n",
       "      <th>Date</th>\n",
       "      <th>NewsText</th>\n",
       "      <th>VADER_Newspaper_Negative</th>\n",
       "      <th>VADER_Newspaper_Positive</th>\n",
       "      <th>VADER_Newspaper_Neutral</th>\n",
       "      <th>VADER_Newspaper_Compound</th>\n",
       "      <th>TextBlob_Newspaper_Sentiment_Polarity</th>\n",
       "      <th>TextBlob_Newspaper_Sentiment_Subjectivity</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Free Trade Has Been a Boon for Energy Independ...</td>\n",
       "      <td>2018-01-01</td>\n",
       "      <td>turbul donald trump' polit career thu far, alw...</td>\n",
       "      <td>0.074</td>\n",
       "      <td>0.136</td>\n",
       "      <td>0.790</td>\n",
       "      <td>0.9790</td>\n",
       "      <td>-0.003281</td>\n",
       "      <td>0.471559</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>France Looks to Deepen Trade Ties With Russia ...</td>\n",
       "      <td>2018-01-01</td>\n",
       "      <td>franc lose appetit trans-atlant trade.\\neconom...</td>\n",
       "      <td>0.085</td>\n",
       "      <td>0.029</td>\n",
       "      <td>0.886</td>\n",
       "      <td>-0.9776</td>\n",
       "      <td>-0.031790</td>\n",
       "      <td>0.374383</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>A Cold War in the Arctic Circle; NATO plans a ...</td>\n",
       "      <td>2018-01-01</td>\n",
       "      <td>arctic region tremend strateg import global tr...</td>\n",
       "      <td>0.037</td>\n",
       "      <td>0.112</td>\n",
       "      <td>0.851</td>\n",
       "      <td>0.9756</td>\n",
       "      <td>0.080676</td>\n",
       "      <td>0.319451</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>World News: France Pursues Wider Trade Relatio...</td>\n",
       "      <td>2018-01-02</td>\n",
       "      <td>franc lose appetit trans-atlant trade.\\neconom...</td>\n",
       "      <td>0.056</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.944</td>\n",
       "      <td>-0.9260</td>\n",
       "      <td>-0.017857</td>\n",
       "      <td>0.351190</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Free Trade Has Been a Boon for Energy Independ...</td>\n",
       "      <td>2018-01-02</td>\n",
       "      <td>turbul donald trump' polit career thu far, alw...</td>\n",
       "      <td>0.073</td>\n",
       "      <td>0.136</td>\n",
       "      <td>0.791</td>\n",
       "      <td>0.9790</td>\n",
       "      <td>0.002749</td>\n",
       "      <td>0.472236</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7640</th>\n",
       "      <td>NaN</td>\n",
       "      <td>2022-10-25</td>\n",
       "      <td>earli day parenthood, infant daughter colicki ...</td>\n",
       "      <td>0.030</td>\n",
       "      <td>0.161</td>\n",
       "      <td>0.809</td>\n",
       "      <td>0.9892</td>\n",
       "      <td>0.194824</td>\n",
       "      <td>0.449275</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7641</th>\n",
       "      <td>NaN</td>\n",
       "      <td>2022-10-25</td>\n",
       "      <td>rishi sunak prime minister. india britain. str...</td>\n",
       "      <td>0.046</td>\n",
       "      <td>0.119</td>\n",
       "      <td>0.835</td>\n",
       "      <td>0.9702</td>\n",
       "      <td>0.067041</td>\n",
       "      <td>0.186123</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7642</th>\n",
       "      <td>Hurricane Ian Claims Hit Profits of Chubb</td>\n",
       "      <td>2022-10-25</td>\n",
       "      <td>chubb ltd. cb -.%\\ndecrease; red point triangl...</td>\n",
       "      <td>0.034</td>\n",
       "      <td>0.076</td>\n",
       "      <td>0.889</td>\n",
       "      <td>0.9201</td>\n",
       "      <td>0.041249</td>\n",
       "      <td>0.417118</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7643</th>\n",
       "      <td>Chip Maker SK Hynix Slashes Capital Spending</td>\n",
       "      <td>2022-10-25</td>\n",
       "      <td>seoul—sk hynix inc., one world’ biggest chip m...</td>\n",
       "      <td>0.048</td>\n",
       "      <td>0.076</td>\n",
       "      <td>0.876</td>\n",
       "      <td>0.9349</td>\n",
       "      <td>0.064406</td>\n",
       "      <td>0.450978</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7644</th>\n",
       "      <td>Emperor Penguins to Be Listed as Threatened Sp...</td>\n",
       "      <td>2022-10-25</td>\n",
       "      <td>emperor penguin soon consid threaten speci end...</td>\n",
       "      <td>0.071</td>\n",
       "      <td>0.106</td>\n",
       "      <td>0.823</td>\n",
       "      <td>0.9283</td>\n",
       "      <td>0.090436</td>\n",
       "      <td>0.368750</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>7645 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                              NewsTitle       Date  \\\n",
       "0     Free Trade Has Been a Boon for Energy Independ... 2018-01-01   \n",
       "1     France Looks to Deepen Trade Ties With Russia ... 2018-01-01   \n",
       "2     A Cold War in the Arctic Circle; NATO plans a ... 2018-01-01   \n",
       "3     World News: France Pursues Wider Trade Relatio... 2018-01-02   \n",
       "4     Free Trade Has Been a Boon for Energy Independ... 2018-01-02   \n",
       "...                                                 ...        ...   \n",
       "7640                                                NaN 2022-10-25   \n",
       "7641                                                NaN 2022-10-25   \n",
       "7642          Hurricane Ian Claims Hit Profits of Chubb 2022-10-25   \n",
       "7643       Chip Maker SK Hynix Slashes Capital Spending 2022-10-25   \n",
       "7644  Emperor Penguins to Be Listed as Threatened Sp... 2022-10-25   \n",
       "\n",
       "                                               NewsText  \\\n",
       "0     turbul donald trump' polit career thu far, alw...   \n",
       "1     franc lose appetit trans-atlant trade.\\neconom...   \n",
       "2     arctic region tremend strateg import global tr...   \n",
       "3     franc lose appetit trans-atlant trade.\\neconom...   \n",
       "4     turbul donald trump' polit career thu far, alw...   \n",
       "...                                                 ...   \n",
       "7640  earli day parenthood, infant daughter colicki ...   \n",
       "7641  rishi sunak prime minister. india britain. str...   \n",
       "7642  chubb ltd. cb -.%\\ndecrease; red point triangl...   \n",
       "7643  seoul—sk hynix inc., one world’ biggest chip m...   \n",
       "7644  emperor penguin soon consid threaten speci end...   \n",
       "\n",
       "      VADER_Newspaper_Negative  VADER_Newspaper_Positive  \\\n",
       "0                        0.074                     0.136   \n",
       "1                        0.085                     0.029   \n",
       "2                        0.037                     0.112   \n",
       "3                        0.056                     0.000   \n",
       "4                        0.073                     0.136   \n",
       "...                        ...                       ...   \n",
       "7640                     0.030                     0.161   \n",
       "7641                     0.046                     0.119   \n",
       "7642                     0.034                     0.076   \n",
       "7643                     0.048                     0.076   \n",
       "7644                     0.071                     0.106   \n",
       "\n",
       "      VADER_Newspaper_Neutral  VADER_Newspaper_Compound  \\\n",
       "0                       0.790                    0.9790   \n",
       "1                       0.886                   -0.9776   \n",
       "2                       0.851                    0.9756   \n",
       "3                       0.944                   -0.9260   \n",
       "4                       0.791                    0.9790   \n",
       "...                       ...                       ...   \n",
       "7640                    0.809                    0.9892   \n",
       "7641                    0.835                    0.9702   \n",
       "7642                    0.889                    0.9201   \n",
       "7643                    0.876                    0.9349   \n",
       "7644                    0.823                    0.9283   \n",
       "\n",
       "      TextBlob_Newspaper_Sentiment_Polarity  \\\n",
       "0                                 -0.003281   \n",
       "1                                 -0.031790   \n",
       "2                                  0.080676   \n",
       "3                                 -0.017857   \n",
       "4                                  0.002749   \n",
       "...                                     ...   \n",
       "7640                               0.194824   \n",
       "7641                               0.067041   \n",
       "7642                               0.041249   \n",
       "7643                               0.064406   \n",
       "7644                               0.090436   \n",
       "\n",
       "      TextBlob_Newspaper_Sentiment_Subjectivity  \n",
       "0                                      0.471559  \n",
       "1                                      0.374383  \n",
       "2                                      0.319451  \n",
       "3                                      0.351190  \n",
       "4                                      0.472236  \n",
       "...                                         ...  \n",
       "7640                                   0.449275  \n",
       "7641                                   0.186123  \n",
       "7642                                   0.417118  \n",
       "7643                                   0.450978  \n",
       "7644                                   0.368750  \n",
       "\n",
       "[7645 rows x 9 columns]"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1 = df1.drop(columns = ['Newspaper_Sentiment_VADER', 'TextBlob_Newspaper_Sentiment'])\n",
    "df1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "f403e9ef",
   "metadata": {},
   "outputs": [],
   "source": [
    "df1['VADER_Newspaper_Positive_Sentiment'] = df1['VADER_Newspaper_Compound'].apply(lambda x: 1 if x > 0 else 0)\n",
    "df1['TextBlob_Newspaper_Positive_Sentiment'] = df1['TextBlob_Newspaper_Sentiment_Polarity'].apply(lambda x: 1 if x > 0 else 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "89f371c4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    7645.000000\n",
       "mean        0.074975\n",
       "std         0.039841\n",
       "min         0.000000\n",
       "25%         0.046000\n",
       "50%         0.070000\n",
       "75%         0.098000\n",
       "max         0.368000\n",
       "Name: VADER_Newspaper_Negative, dtype: float64"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1['VADER_Newspaper_Negative'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "8cfd75df",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    7645.000000\n",
       "mean        0.306452\n",
       "std         0.828290\n",
       "min        -0.999800\n",
       "25%        -0.750600\n",
       "50%         0.877900\n",
       "75%         0.981800\n",
       "max         0.999900\n",
       "Name: VADER_Newspaper_Compound, dtype: float64"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1['VADER_Newspaper_Compound'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "18db1449",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    7645.000000\n",
       "mean        0.060846\n",
       "std         0.071511\n",
       "min        -0.333333\n",
       "25%         0.018001\n",
       "50%         0.058219\n",
       "75%         0.100077\n",
       "max         0.645455\n",
       "Name: TextBlob_Newspaper_Sentiment_Polarity, dtype: float64"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1['TextBlob_Newspaper_Sentiment_Polarity'].describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "56e62e5e",
   "metadata": {},
   "source": [
    "### Plot the Sentiment Scores"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "21c660b5",
   "metadata": {},
   "outputs": [],
   "source": [
    "df1_2 = df1.copy()\n",
    "df1_2['Month'] = df1_2['Date'].dt.strftime('%Y-%m')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "0e02b0c9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Index: 58 entries, 2018-01 to 2022-10\n",
      "Data columns (total 8 columns):\n",
      " #   Column                                     Non-Null Count  Dtype  \n",
      "---  ------                                     --------------  -----  \n",
      " 0   VADER_Newspaper_Negative                   58 non-null     float64\n",
      " 1   VADER_Newspaper_Positive                   58 non-null     float64\n",
      " 2   VADER_Newspaper_Neutral                    58 non-null     float64\n",
      " 3   VADER_Newspaper_Compound                   58 non-null     float64\n",
      " 4   TextBlob_Newspaper_Sentiment_Polarity      58 non-null     float64\n",
      " 5   TextBlob_Newspaper_Sentiment_Subjectivity  58 non-null     float64\n",
      " 6   VADER_Newspaper_Positive_Sentiment         58 non-null     float64\n",
      " 7   TextBlob_Newspaper_Positive_Sentiment      58 non-null     float64\n",
      "dtypes: float64(8)\n",
      "memory usage: 4.1+ KB\n"
     ]
    }
   ],
   "source": [
    "df1_3 = df1_2.groupby(['Month']).mean()\n",
    "df1_3.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "5cb31919",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x1152 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axes = plt.subplots(ncols=1, nrows=2, figsize=(14, 16))\n",
    "\n",
    "sns.set_style(\"white\")\n",
    "plt.sca(axes[0])\n",
    "sns.lineplot(data=df1_3, x=\"Month\", y=\"VADER_Newspaper_Compound\")\n",
    "plt.xlabel('Month')\n",
    "plt.xticks(rotation = 90)\n",
    "plt.ylabel('Sentiment Score')\n",
    "plt.title('VADER Sentiment Analysis of Wall Street Journal Newspaper',  weight = 'bold', fontsize = 16)\n",
    "\n",
    "plt.sca(axes[1])\n",
    "sns.lineplot(data=df1_3, x=\"Month\", y=\"TextBlob_Newspaper_Sentiment_Polarity\")\n",
    "plt.xlabel('Month')\n",
    "plt.xticks(rotation = 90)\n",
    "plt.ylabel('Sentiment Score')\n",
    "plt.title('TextBlob Sentiment Analysis of Wall Street Journal Newspaper',  weight = 'bold', fontsize = 16)\n",
    "\n",
    "plt.savefig('Figure1.png')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "1e8d8af6",
   "metadata": {},
   "outputs": [],
   "source": [
    "freq = df1['VADER_Newspaper_Positive_Sentiment'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "bd9eb2e4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1224x720 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axes = plt.subplots(ncols=2, nrows=1, figsize=(17, 10))\n",
    "\n",
    "# VADER Newspaper Sentiment\n",
    "freq = df1['VADER_Newspaper_Positive_Sentiment'].value_counts()\n",
    "labels = ('Positive', 'Negative')\n",
    "sizes = [freq[1], freq[0]]\n",
    "explode = (0.1, 0)\n",
    "plt.sca(axes[0])\n",
    "plt.pie(sizes, labels=labels, explode=explode, autopct='%1.1f%%', shadow=True, startangle=0)\n",
    "plt.title('VADER Newspaper Sentiment', weight='bold').set_fontsize('14')\n",
    "\n",
    "# TextBlob Newspaper Sentiment\n",
    "freq = df1['TextBlob_Newspaper_Positive_Sentiment'].value_counts()\n",
    "labels = ('Positive', 'Negative')\n",
    "sizes = [freq[1], freq[0]]\n",
    "explode = (0.1, 0)\n",
    "plt.sca(axes[1])\n",
    "plt.pie(sizes, labels=labels, explode=explode, autopct='%1.1f%%', shadow=True, startangle=0)\n",
    "plt.title('TextBlob Newspaper Sentiment', weight='bold').set_fontsize('14')\n",
    "\n",
    "plt.savefig('Figure2.png')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4fe8c303",
   "metadata": {},
   "source": [
    "**Based on the line plots and pie plots of the sentiments shown in Wall Street Journal news, we could detect the trends of sentiment score changes utilizing two types of lexicons -- VADER and TextBlob are consistent with each other. As for the proportion of negative words, the results are slightly different. Specifically, the proportion of negative text shown in Wall Street Journal news using VADER lexicon is 25.4% and 6.7% based on TextBlob lexicon.**"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2d6a3c5c",
   "metadata": {},
   "source": [
    "# Stock Data Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "81042dc9",
   "metadata": {},
   "outputs": [],
   "source": [
    "df2 = pd.read_csv('SP500.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "8f7c87f4",
   "metadata": {},
   "outputs": [],
   "source": [
    "df2_1 = df2.rename(columns = {'Index':'Date', 'GSPC.Adjusted': 'SP500_adj_price', 'GSPC.Volume': 'SP500_volume'})\n",
    "df2_1['Date'] = df2_1['Date'].astype('datetime64')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "d2aa2494",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 1216 entries, 0 to 1215\n",
      "Data columns (total 3 columns):\n",
      " #   Column           Non-Null Count  Dtype         \n",
      "---  ------           --------------  -----         \n",
      " 0   Date             1216 non-null   datetime64[ns]\n",
      " 1   SP500_adj_price  1216 non-null   float64       \n",
      " 2   SP500_volume     1216 non-null   float64       \n",
      "dtypes: datetime64[ns](1), float64(2)\n",
      "memory usage: 28.6 KB\n"
     ]
    }
   ],
   "source": [
    "df2_1.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "a93827e3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DatetimeIndex(['2018-01-01', '2018-01-02', '2018-01-03', '2018-01-04',\n",
       "               '2018-01-05', '2018-01-06', '2018-01-07', '2018-01-08',\n",
       "               '2018-01-09', '2018-01-10',\n",
       "               ...\n",
       "               '2022-10-22', '2022-10-23', '2022-10-24', '2022-10-25',\n",
       "               '2022-10-26', '2022-10-27', '2022-10-28', '2022-10-29',\n",
       "               '2022-10-30', '2022-10-31'],\n",
       "              dtype='datetime64[ns]', length=1765, freq='D')"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "start = datetime.strptime('01-01-2018', '%m-%d-%Y')\n",
    "date_generated = pd.date_range(start, periods = 1765)\n",
    "date_generated"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "72f2cee1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Date</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2018-01-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2018-01-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2018-01-03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2018-01-04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2018-01-05</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Date\n",
       "0 2018-01-01\n",
       "1 2018-01-02\n",
       "2 2018-01-03\n",
       "3 2018-01-04\n",
       "4 2018-01-05"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2_2 = pd.DataFrame(date_generated)\n",
    "df2_2 = df2_2.rename(columns = {0:'Date'})\n",
    "df2_2['Date'] = df2_2['Date'].astype('datetime64')\n",
    "df2_2.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "e1f1a73e",
   "metadata": {},
   "outputs": [],
   "source": [
    "df2_3 = pd.merge(df2_1,df2_2, how='right', left_on = 'Date', right_on = 'Date')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "f6f771e7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>SP500_adj_price</th>\n",
       "      <th>SP500_volume</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2018-01-01</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-02</th>\n",
       "      <td>2695.810059</td>\n",
       "      <td>3.397430e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-03</th>\n",
       "      <td>2713.060059</td>\n",
       "      <td>3.544030e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-04</th>\n",
       "      <td>2723.989990</td>\n",
       "      <td>3.697340e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-05</th>\n",
       "      <td>2743.149902</td>\n",
       "      <td>3.239280e+09</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            SP500_adj_price  SP500_volume\n",
       "Date                                     \n",
       "2018-01-01              NaN           NaN\n",
       "2018-01-02      2695.810059  3.397430e+09\n",
       "2018-01-03      2713.060059  3.544030e+09\n",
       "2018-01-04      2723.989990  3.697340e+09\n",
       "2018-01-05      2743.149902  3.239280e+09"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2_3.set_index('Date', inplace = True)\n",
    "df2_3.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "cbc522d8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>SP500_adj_price</th>\n",
       "      <th>SP500_volume</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2018-01-02</th>\n",
       "      <td>2695.810059</td>\n",
       "      <td>3.397430e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-03</th>\n",
       "      <td>2713.060059</td>\n",
       "      <td>3.544030e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-04</th>\n",
       "      <td>2723.989990</td>\n",
       "      <td>3.697340e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-05</th>\n",
       "      <td>2743.149902</td>\n",
       "      <td>3.239280e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-06</th>\n",
       "      <td>2743.149902</td>\n",
       "      <td>3.239280e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-10-27</th>\n",
       "      <td>3807.300049</td>\n",
       "      <td>4.687320e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-10-28</th>\n",
       "      <td>3901.060059</td>\n",
       "      <td>4.459410e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-10-29</th>\n",
       "      <td>3901.060059</td>\n",
       "      <td>4.459410e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-10-30</th>\n",
       "      <td>3901.060059</td>\n",
       "      <td>4.459410e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-10-31</th>\n",
       "      <td>3901.060059</td>\n",
       "      <td>4.459410e+09</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1764 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            SP500_adj_price  SP500_volume\n",
       "Date                                     \n",
       "2018-01-02      2695.810059  3.397430e+09\n",
       "2018-01-03      2713.060059  3.544030e+09\n",
       "2018-01-04      2723.989990  3.697340e+09\n",
       "2018-01-05      2743.149902  3.239280e+09\n",
       "2018-01-06      2743.149902  3.239280e+09\n",
       "...                     ...           ...\n",
       "2022-10-27      3807.300049  4.687320e+09\n",
       "2022-10-28      3901.060059  4.459410e+09\n",
       "2022-10-29      3901.060059  4.459410e+09\n",
       "2022-10-30      3901.060059  4.459410e+09\n",
       "2022-10-31      3901.060059  4.459410e+09\n",
       "\n",
       "[1764 rows x 2 columns]"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2_3 = df2_3.fillna(method=\"ffill\", axis = 0)\n",
    "df2_3 = df2_3.iloc[1:, :]\n",
    "df2_3"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0434da0a",
   "metadata": {},
   "source": [
    "### Time Series Plot for Stock Price and Daily Changes of Stock Price"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "b0f8a066",
   "metadata": {},
   "outputs": [],
   "source": [
    "import sys\n",
    "\n",
    "import pandas_datareader.data as web\n",
    "\n",
    "import statsmodels.formula.api as smf\n",
    "import statsmodels.tsa.api as smt\n",
    "import statsmodels.api as sm\n",
    "import scipy.stats as scs\n",
    "import statsmodels.tsa as smta\n",
    "\n",
    "import matplotlib as mpl\n",
    "%matplotlib inline\n",
    "p = print"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "373a4af0",
   "metadata": {},
   "outputs": [],
   "source": [
    "def tsplot(y, lags=None, figsize=(10, 8), style='bmh'):\n",
    "    if not isinstance(y, pd.Series):\n",
    "        y = pd.Series(y)\n",
    "    with plt.style.context(style):    \n",
    "        fig = plt.figure(figsize=figsize)\n",
    "        #mpl.rcParams['font.family'] = 'Ubuntu Mono'\n",
    "        layout = (2, 2)\n",
    "        ts_ax = plt.subplot2grid(layout, (0, 0), colspan=2)\n",
    "        acf_ax = plt.subplot2grid(layout, (1, 0))\n",
    "        pacf_ax = plt.subplot2grid(layout, (1, 1))\n",
    "        #qq_ax = plt.subplot2grid(layout, (2, 0))\n",
    "        #pp_ax = plt.subplot2grid(layout, (2, 1))\n",
    "        \n",
    "        y.plot(ax=ts_ax)\n",
    "        ts_ax.set_title('Time Series Analysis Plots')\n",
    "        smt.graphics.plot_acf(y, lags=lags, ax=acf_ax, alpha=0.5)\n",
    "        smt.graphics.plot_pacf(y, lags=lags, ax=pacf_ax, alpha=0.5)\n",
    "        #sm.qqplot(y, line='s', ax=qq_ax)\n",
    "        #qq_ax.set_title('QQ Plot')        \n",
    "        #scs.probplot(y, sparams=(y.mean(), y.std()), plot=pp_ax)\n",
    "\n",
    "        plt.tight_layout()\n",
    "    return "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "27f4d307",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x576 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "tsplot(df2_3['SP500_adj_price'])\n",
    "plt.savefig('Figure3.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "b21419b0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x576 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "tsplot(df2_3['SP500_volume'])\n",
    "plt.savefig('Figure4.png')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1920702f",
   "metadata": {},
   "source": [
    "**In reality, time-series stock data is always non-stationary; for example, the plot for S&P500 stock price and total volume above are non-stationary. And the autocorrelation (ACF) and partial autocorrelation (PACF) plots testify to the autocorrelation.**\n",
    "\n",
    "**Next, I will do AD Fuller tests for each stock series to detect the stationarity.** "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "760f2f26",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The p-value for the ADF test on S&P500 adjusted stock price is 0.6988742967077024\n",
      "The p-value for the ADF test on S&P500 total volume is 5.7408226686442366e-05\n"
     ]
    }
   ],
   "source": [
    "# Compute the ADF for the stock data to detect stationarity\n",
    "# The null hypothesis for each test is that the stock data is non-stationarity\n",
    "from statsmodels.tsa.stattools import adfuller\n",
    "SP500 = adfuller(df2_3['SP500_adj_price'])\n",
    "print('The p-value for the ADF test on S&P500 adjusted stock price is', SP500[1])\n",
    "SP500_volume = adfuller(df2_3['SP500_volume'])\n",
    "print('The p-value for the ADF test on S&P500 total volume is', SP500_volume[1])\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2e93516e",
   "metadata": {},
   "source": [
    "**From the results of ADF test, we could see the p-value for S\\&P500 stock data is 0.70, much larger than 0.05, so we could not reject the null hypotheses, which leads to the conclusion that the S\\&P 500 stock data is non-stationary. However, the p-value for the ADF test of S\\&P500 total volume data is smaller than 0.05, thus we could reject the null and conclude that stock total volume data is stationary.**</font>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "beeb8d1f",
   "metadata": {},
   "source": [
    "**Working with non-stationary data is difficult. To model, we need to convert a non-stationary process to stationary.** \n",
    "\n",
    "**First-difference is often used to convert a non-stationary process to stationary.** "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "ed4399ec",
   "metadata": {},
   "outputs": [],
   "source": [
    "# S&P500 Stock Data\n",
    "SP500_adj_price1d = np.diff(df2_3['SP500_adj_price'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "96129dd9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The p-value for the ADF test on first-difference of S&P500 stock data is 1.1453443296439017e-15\n"
     ]
    }
   ],
   "source": [
    "SP500_adj_price1d_ad = adfuller(SP500_adj_price1d)\n",
    "print('The p-value for the ADF test on first-difference of S&P500 stock data is', SP500_adj_price1d_ad[1])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1c809827",
   "metadata": {},
   "source": [
    "**After getting the first-difference of S&P500 stock data, through the Dickey-Fuller test, p-value for the ADF test on first difference of S&P500 stock data is 1.1453443296439017e-15, which is smaller than 0.05, thus we could conclude that the first difference gives us stationary white noise w(t).**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "c2235880",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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8DQ8Po6WlJaeOKRvOUyAQQGdnZ04dUzacp1hs5/cxeSEgan1sxlgFgMcB3ALgOs75dP31IwGcD+BpALtxzn+iv34XgLs4588bt7N06dLowoULhWjyQkdHB+rq6jK+31yHfJUD+SqHdHxd3zmEbzyiTch79sLF+N2LG/Dy+u3jf09mqL2KhzyVA/kqh2zxdfny5e8tWbJkHzefFTJJjzE2E8BLAO7mnN8LYMzwdgWA7QB69X+bX88KBgYG/JaQk5CvciBf5UC+yoF8FQ95KgfyVQ4q+pp2gMwYawDwLICfcM5v019+nzF2uP7v4wC8BuBtAIcwxooZY1UAdoY2gS8raGxs9FtCTkK+yoF8lUM6vponaas4a1sW1F7FQ57KgXyVg4q+ihhBvgxANYBfMMZeZoy9DOByAFcwxpYCKATwIOe8FcBfoAXLLwL4Oed8WMD+hdDa2uq3hJyEfJUD+SoH8lUO5Kt4yFM5kK9yUNHXtCfpcc4vAXCJxVuHWXz2Fmg5ylmH1+Rtwh3kqxzIVzmQr3IgX8VDnsqBfJWDir7SQiE6VVVVfkvISchXOZCvciBf5UC+ioc8lQP5KgcVfaUAWaejo8NvCTkJ+SoH8lUO5KscyFfxkKdyIF/loKKvFCDrqHh3owLkqxzIVzmk42vCJL00teQS1F7FQ57KgXyVg4q+UoCsEwqF/JaQk5CvciBf5SDSV6siFm9v7sGDK9oS38hxqL2KhzyVA/kqBxV9FbGSXk4wNDTkt4SchHyVA/kqh3R8dTNifPkz6wEAi3eowLza0pT3pRrUXsVDnsqBfJWDir7SCLKOijX6VIB8lQP5KodM+do7HMnIfrIFaq/iIU/lQL7KQUVfKUDWUbFGnwqQr3IgX+WQjq8Byjq2hdqreMhTOZCvclDRVwqQdQoLC/2WkJOQr3IgX+Ug0lcKlyeg9ioe8lQO5KscVPSVAmSdiooKvyXkJOSrHMhXOaTlqzkipqWmx6H2Kh7yVA7kqxxU9JUCZJ3Ozk6/JeQk5KscyFc5kK9yIF/FQ57KgXyVg4q+UoCsU11d7beEnIR8lQP5Kod0fDWOF0ej0fTF5BDUXsVDnsqBfJWDir5SgKyjYgkSFSBf5UC+yiEdX6OmfxsD5nBkLOXt5gLUXsVDnsqBfJWDir5SgKwzPDzst4SchHyVA/kqB1G+RqPxAfIDK7YJ2a6qUHsVD3kqB/JVDir6SgGyjoo1+lSAfJUD+SqHtHx1yKr4uK0/9e3mANRexUOeyoF8lYOKvlKArKNijT4VIF/lQL7KQZSv5lh5stdIpvYqHvJUDuSrHFT0lQJkneLiYr8l5CTkqxzIVzmk42vUEBabJ+lN9opv1F7FQ57KgXyVg4q+UoCsU1JS4reEnIR8lQP5KgdRvl708KfoG5lYTnqSx8fUXiVAnsqBfJWDir5SgKzT3d3tt4SchHyVA/kqh3R8NY4ZN/eMYOmmHvsPT7KImdqreMhTOZCvclDRVwqQdWpra/2WkJOQr3IgX+Xg1dfIWBRDYW2k2Kn0cUKKxSQrk0ztVTzkqRzIVzmo6CsFyDp9fX1+S8hJyFc5kK9y8OrrNx7+FCfeuQLbh8K46OFPbT832SfpUXsVD3kqB/JVDir6SgGyTigU8ltCTkK+yoF8lYNXX5u2a7U9H1vV4fzByR0fU3uVAHkqB/JVDir6SgGyjoo1+lSAfJUD+SqHjPk6yQJmaq/iIU/lQL7KQUVfKUDWUbFGnwqQr3IgX+WQqq/N251XiUqIhydZDjK1V/GQp3IgX+Wgoq8UIOuoWIJEBchXOZCvckjV11c2bHd8f5INGCdA7VU85KkcMunru829uOPdrQl103MRFdtrvt8CsoXCwkK/JeQk5KscyFc5yPJ1si8UQu1VPOSpHDLp62VPrwMAsPoyHDi7KmP79QMV2yuNIOv09DjULCVShnyVA/kqh4z5OskCZmqv4iFP5eCHrz3DoxnfZ6ZRsb1SgKxTV1fnt4ScJNt8fX9LH9Z1DvotI22yzddcQZ6vpog495+oxkHtVTzkqRz88DXdJ0wdAyFExrK7U1GxvVKArKPi3Y0KZJOvXYNh/OSptfjmI9xvKWmTTb7mErJ8newpFtRexUOeysEPX9PpHla09OGMf6/Er55bL0yPDFRsrxQg64TDYb8l5CTZ5Ov2odx5jJVNvuYSsnyd5PExtVcJkKdy8MPXdG6gn1vTBQB4e3OvIDWp0zs8ip8/vQ5LmxKDYRXbKwXIOirW6FMB8lUO5KscZPmacP2bZBEztVfxkKdy8MPXdFbazKYCGHcvb8E7zb2Wo9kqtlcKkHVUrNGnAuSrHMhXOUjzdZIFxGaovYqHPJVDMl8fWNGGRz7eliE1YhiLRjMyEbBz0H4fKrZXYWXeGGP7A7iGc344Y2w+gDugTUX5GMDFnPMxxtjXAFwEYBTAlZzzJ0XtP13Kysr8lpCTkK9yIF/lIMvXABBf6zSLRn0yAbVX8ZCn6fHAijb899MO/PnzO2FKScH4606+hiJjuOXtrQCAk3edmvK+B0IR9I9Exv8OpnED7aYr+eWz6/H25l7cdBLD/LrS1HeWhFBkzPY9FdurkBFkxtiPAfwTQLH+0h8BXM45PwTateFExlgjgO8C+AyAYwBczRgrErF/EeTl5fktISfJJl9zaaJUNvmaS8jyNRAIwDjJfCybnotmAGqv4iFP0+OWt7dia28Ij3zcHve6k6+ifrZfvHsFzr5/5fjfAYeLU7JFRNxIiuUnv7iuG399YzP+9NomVzq9MjJqHyCr2F5FpVisA3CK4e+9Abyi//spAJ8FsB+ANzjnI5zzHgBrAewuaP9p09vrf4J7LkK+yoF8lUOmfJ1c4TG1VxmQp2Iw/xYz4au5IptdeHzVixvwrUe5Ywk3L6vwRaNRPPFJB57inQg5BLOpMuywTRXbq5AUC875Q4yxOYaXApzz2FnrA1AFoBKAcWpj7PU4RkdH0dTUhLq6OvT09CAcDqOxsRGtra0oKytDXl4eent7UV9fj66uLkSjUdTX16OtrQ3l5eUAgP7+fjQ0NKC9vR2BQAA1NTVob29HZWUlIpEIBgYGxrdZUFCAqqoqhMNhbN++HaFQCENDQ+PvFxYWoqKiAp2dnaiursbQ0BCGh4fH3y8uLkZJSQm6u7tRW1uLvr4+hEKh8fdLSkpQWFiInp6ejB9TR0cHqqqqfD2miooKNDc3Z8Ux9Y9M3A+2tLQofZ7C4TDa2tqo7Qk+pqKiInR0dLg+JreEw2FsbGoa/7utrQ0j9UWT5jxFo1E0NTXl1DH5fZ7C4TBaWlpy6pgyeZ7GA5G+XmzeHBk/poKCAnR2do4fE9/UitLCIHaYWofm1onc440bN6KhsRHb9O94OSYzvb096O3NSzimV9ZvBwB8tGEr5tSWWR7TwMDA+Haamposz1OMHkOQurl5C6ZNrRV6nvqHRuL6OON5iunzu+15ISBqDXA9QL6Pc34AY6yZcz5Df/1EAEcBeBbAsZzzb+mvPwLgKs75u8btLF26NLpw4UIhmrzQ3NyMGTNmZHy/uU42+bqhawgXPfwpAODZCxf7rCY9ssnXXMKrr0f/831XnztyXjV+cOgsnHD7hwCAq46Zh31nVqakUUWovYpnsnr6zuZe3PrOVvzsiNmYXV2S8nZiv93T9mjABftOH3/d6GvXYBhfufdjFOUH8cS5e2AoHMGJd64AAHz7oBm4/d0WXHv8fCzwmNdr7jcuP3IOdmssx5rOQew7o3I85SL2ubtPW4SGCuulmn//0ka8uK4bgP11LbadkxfV45GVWkrJI+fsjrJCsWkP5/5nFbb2joxriUajeHRlO1a2DeCcnfIxa+ZMrG4fRGlhEDOqipNsTQ7Lly9/b8mSJfu4+aysKhbvM8YO1/99HIDXALwN4BDGWDFjrArAztAm8GUFom4UiHjIVzmQr3KQ5WsgEJ+/ONlykHOpvY6MjjnmWmaKXPLUCz9/Zh3Wdw3hulfl5NEafV2rr7oaO99Gy298sxkDoQj+9b6A6gwB4IIHP8Hlz6zHaxu3AwBGDWkVQYdIzUsrMGZqyFh5z/i76BkexRfv/gg3L9uCVzdsx6qOEPpGRvHtxzjOf+AT4fuWgawA+QcArmCMLQVQCOBBznkrgL9AC5ZfBPBzzvmwpP17pr6+3m8JOUk2+WqcB6H6xSWbfM0lZPkaQPyF7BfPelv16pNtAzjrvo/x9mb1VqMCcqu9nnjnh/j8HR/63ofkkqepIOsmxeiruSiD1RmvKfH22N6KIALoD2lVLT5u1VImjMdn19Q6B8N4SR89dkPUcAQRw0ZFtWVj0P3s6s7xYwKA0opK5RbrElbmjXO+EcAB+r9XAzjM4jO3ALhF1D5F0tbWhtmzZ/stI+fIJl/NVbZULmqRTb7mEjJ9TecS9Ovn1qN7aBSXP7NeyfSgXGqvsRggEgXyfexEcsnTVEinNJoTRl/No6xWgWRNqYAwyuJYjBPe7J443frOVk+7GRtL/PdQOIJvPvIpDpxVhYsOSC9lJ2rzbwD465tb8KMjdkxr+5mGFgrRiSV8E2LJJl/jAuQo8NamHmVH5LLJ11zCi69eRl2eX9uNBz+KX1zgrvda8Mp6d6M/Mh6HZpJcaa9jEkbdUiVXPE0Vp9JoXni3uTfuXBp9HTX97mT9DI1HEpMyHE4+gjwcjli/YcOYxQjyGxt7sLU3hIdM5e7SxqS5JxTF5c94e3LmNxQgE5OGuMdLY1H84tn1yv1giezB67Xyrvda4v6+5/1WXPXixrT2FRmL4uGPt2Fj95BHNUQqTOK1XrIOY1DZPhDCu82plRFb1zk0nvdrxpiGMBaNWp7ziICGYIz1N/cMY3QsGpdiYReYe71HM44gy77pzoXfBwXIOv39/X5LyEmyyde4CQqmjk81ssnXXMKLr9nQbJ5e3Ym/L9uCrz/0qd9SHMmV9hqft+mjEOSOp6kSNESVZ/57JS57eh1WtPSltK0VLRNeGn01BpGfv+NDPLO6M+G7YwICzYAh3F++pQ+/fHYdRgwJ0NahuXeM17qIhAbs91MV0VCArNPQ0OC3hJwkm3w1/naNHZ+Kv+ls8jWX8OKrjBurjd1DWKfPnHdDU3fWzHN2JFfaazZVIskVT1MlENDyZ7f1h8Zf+3Sb+9+OEWOMa/TVeJ0IR6L459uJOb8i2oE5n/rd5r64HORwJIq+kfQnuBnnHMofQVbwwmqCAmSd9nbB+TcEgOzyNf7uGYZ/q/dDziZfcwkvvopqNTe+uXn8319/6FN88xGekPuoYBONw62v0WgU/3q/Fa/bPPL2G2Mf8lFrv69Bskp9QDQaxS1vbcHTPHEENlUCAM6+byXOum9iyWbz78YtxpFPo69u0idEp1jEGDVs+KKHP8UX7/4IPcPeg2RjIGwc7TZX6BCN6n0WQAHyOKIS/t2i+qQbt2TaVyeMjsd1GgqeimzyNZfw4quoC8DjqzoSXgubrl4KNtE43Pq6oWsYd77Xgt88v0GyotQw9hWXP7MeT1icu0yhUh+wsXsYD3y0DX98TVzt4mAggN6R+ElqqQbIxq8ZfXVznXZ7LQ+NjmHIw6S6nz+zLuE13j5g8UmHfUbG8JV7J5abMCod1TswYzN6bo39DczW3hHc+vYW9FoE6QOhCP6xrDnhfKgOBcg6NTU1GdvXO5t7cdxtH+CpT/3rXDNFJn1NxphN3rGKeVPZ5Gsu4cVXvx+xA+qUKnTrayhifKzs/2IcZsx9hZcatKJRqQ+QUbPY6v5AxMBTb7AMf3ilCR0DIVcBt9ug/Mv/+ggn3rnCsl0HXP6S3X4uxoauobhR5/gR5ETdf3jF/gbm+0+sxv0rtuGGNzYnvHfr21sTqmAk6x5VuO5SgKyTycdVf9Lvov/0emJDyzWy6TFgfA7yxL9VHEHOJl9zCT99dQq4bS8mWRAhdw+G8fOn1zlWEXDrqzFA7hgMp61NNNnUV6jUBwQljHZbpiWMRTEY8j6KGY1qAWNL3wh+8sxGPLemC394pclV+p3bAHlQL9tmNcoq62GAOaA2huaxYNntrrv0RT6s5khs7kmcC5HMlWz6LdlBAbJOZWVlxvalQLsQRiZ9TUYuVbHIJl9zCS++iuzgR8eipglg4rYtm1vf2Yp3mntx2dOJj4RjuPV10FD7NRtX3TL3FVYTkdZ1DuKMf3+MVzfIHV1OtQ9Y0dKHXzyzDh0DoeQfFoSIRT3CkTHcs3yiVGLQIrR74KNtOOmuFdjQ5a3sYRRRXPtKE756/yrE4tetvSFXI9KjY1F83NqPZy0qXNjsLAG39qQbSBtHkFv6Qvj3B61xvzlX27DQb3UJTR4gZ38nRwGyTiSSudwZFR4tiCJTvq5uH8Rt72xFyPFRnvXjJZWCkRiZbK+TCb98Pf62D/DvD1rH/1apj+hzkXcYiURcBRvGHM1svICaD8HqkK59uQkdA2Fc+cLGlPfTPhDCIx9vc8xZTbWt/vC/a/HW5l7ckMEnmCJGSG9eugV3LZ/4jTht82m3warOWNQ6XcZtgPz9J9fgulc3ZX09cuPV8dpXmnD7uy24eVmzp2209iXeWI1ZhcNJfr8qXHcpQNYZGPCW/J4OCrQLYXjxtXswjH8sa0ZL74jl+y29I1jdbl3G59uPcdz3YRse+nib5fuAwwiyCr9UE2Zfo9EonuadWNuRWpkjQsNLexUdwBkv/uYm6XZPY9EomrqHMhtguwh+3t28Hcfd9gGuemGDozbjaJbVz3IoHJF2bO9v7cOP/rvGtv8B3E3MNFc1GAhFPJfo+t7jq3Hzsi341/uttp8xt9XQ6Bg+au13nYfb6VMKSyp5wm9t6sGTpjk7IpeatlIUdVmozHg8nQOpeZpqi3b6XjQaTfhtWl3rwjZlODZvH7b9LbSZg+QUDiAbb4DNUICs09jYmLF9KdAuhOHF1z+82oSHPm7HD/+7xvL9r/5nFb79GEf3kH0nZHV3G8OuDnL2TQVKjtnXFS39+ONrm/CtR7lPinIDL+1V5u841U3f/m4LvvbQp7h7uX1g5Qf3rdYutK9s2I7zHliFW97aYvm5IUP+qDmQ2tQ9jBPvXIE/vNKUkoaW3hHH/NSf/G8tPmzpx+9f3mj7GXNOqlUbMF/4T75rBb5490eeAsN2PdBa7XDDa26r173ahB88uSbuSYQT4QwODBhjsFQqTTy/pivhNacqHl5jZ6ubLre/b6OPb2/uxebtifm4cRPEMzBE9pc3NuO8B1YlPFF1u+fQ6BguePATfPU/qyzf7zJdg61vMJxRIQ6iAFmntTVzFxQF2oUwvPi6Xs8ba09yF97lMPLh9NgtvoqF9euqYPa1xeHGIF1e3dCN/3zYJm372YSX9iqz1ZhHetw20fv183T/Cvnn6/0tfbhpabNj4DcQimBr7whGRydGULf2hvDAR9ZPeuJHkOO3+xTXRhCfX+s9t7elbwRf/c8qnH3/RM3cyFgU3RZ9yYYu+8VXzOdhdccgPmp1t6JdKoFhWUGe7Xvmtvry+u0AgKdc1hoeFVHA10A0GsWKlj70W4yWG9tIKj5YDWI4jSB73UMqObQxjMf2yMp2XPDgJ46fsTp+r5egbf0hXPb0Wtu29+QnHdjaG8LyLfErC7q91g0kKUeXZ7rQWvqXZFcqXHfz/RaQLRQUFGRsXyrlF6aLF19FlLpx2oLR9fji6a52m1WYfRX5uNFMLJfywNlVmDmlWN6OsgAv7VVmB++2SdqddvMFTAY/eWqt4/tP8c7xij1uic9Bjn8vnZ/pJ21aOoIxX/rXz63HW5t78dcTdwKrL0NRfhAjo2NxK5iZsTrnP3hyDZ69cLGlbrvSkm4pL7IPkO3aqtv4Myy443t5/XZc/dJGzK8twU0nLzRpmhCllTmzPy4rrK6ZXkueOW7fdr/Jv9vWn3xwwnhO0rkxiR3xn1/fhHebky+rbe4G3LaNZE3DfL1JZdU8FTIbaQRZp6qqSsp2rUZXFGgXwkjV1069dNTyLYmlo5yu/U6dpt069Jl45CUas68yyiiZGfI421lFjL6+ur7b8RG31BSLaBRrOgaxtKlH+9v0Xgy7s55vccf06oZufOuRTxPzByWRLDi2CnrMI8htfaHxJ0aib2Tf2qz1LS/qk7PKC5MHbW4u6sbjSncycKmDJru+1XzNGR4dw2Mr29FuqlohegT51fWaj2s7h7CyrR8/+d9abOkZ0TUZ9pvKCLLFV5wGBbz2hpY3L1F31+pky70/urIdj62aKMlnOYLs8RrktsKLOQ3F7U1asvJ25stNKiPwKlx1KUDW6ehwv2jHx639rkr4tPSO4LjbPsA/PM4SzSW8+Gr8zf19WTPeae7FT59KLB2V6uiYbR1kBeM+s68yR5DHyYKau7KJ+bpp+zCufHEjvu2Q0y01xSIKXPwox6+eW49tphGqtzfb1xuOkWdoEK+u78ZlT6/FlS9sxNrOIfzDJv/Xiic/6cB9H8pJP7ObhBdjODyGs+9fOb4SmFMQEY1GcdULG1Lqa2P9iZvm7SbAMB7XT/43McoeGYsiNDqGFS19iIxFEY1Gky6gUZRnf4m261vNGu96rwV/W9qMSx5fHfe66Bxk483NpU+swftb+/DLZ9dhLBqNH0FOKUCWHE7ZBHjp7jcyFsVNS5vxz7e3xr2WKrFLX6qbcHs4buo/x23Xcl/O21BhcjwFyDpeRjq//+QaXPnCxoQ78tXtg2g2FMyO3TW6WWEmMhbF46vaLRP8VcbTCLLhCmUuHRW38p1TaOKYgzzxb9XrIJt9zcSys6+s68b6zuwuY5QuMV+3O0wEjSEzVcq46b6R0bgXPtjah189ux4fbrV/xBqLq6LRKK58cWPc41gvK9T95Y3NuO2dFsvlZa3wMjpo9bszPqXoM02oc7qRbR8I45UN2xP6WjeMj7Yn+QmtbOt3VRLL2D993DZRaSISjeL61zbhh/9di7uWt+AXz67H5+/40HFOxUAoglc3dFsG0nZ9q/kUrNI1dJjmdvSNRBJuvtJh0CJvdXPPCH7837XxObj6yHXnYBhfe+gTVznTVj+1txxuFM2nMln7HbLwN+p2CNkBq2A4nRzk2BNSt32P+XNuA99kwevW3uTtJtmeVBiXogBZJxTy3lEYf3R9I6P49mMc5z8wkaBv1xatXv7fpx248c1mywR/lUnFVyC+g7vrvRYMhOxzE40djnMOsvWjT6c+4+3NPfh0m/cSgP0jo1IDKLOvDgNNwnjgo234xiOfWq6k5JaR0THc9V5L1tYLjfmaH5ww1C7ok3lbZbyQFeQF4/b10MftWLqpBz/631rbgDA2KmqVFhMMBPDn1zfhe4+vTriA241u2ZWCMnP8bR/gE5e/F6tdGUchnzYETv/+oDUuFaqlbwQr2yYmKKXzU7OKj62C1kufWGOb9xmNu+G23o+x1u7TvHP8ScCyTT222p74pANXvrARp97zEXi7qaybTd9qvvFwerr059e95Yg7MWBTIWRFa39ce479nv71fiuauodd5amnM9h413st+NI9H+Flh2XBLZ/KRNMP4qwCUusUC5fERpBt3o6aRuvNu3f7tDTZPfRvX9gQV9LOLkXFCRXmYlGArDM05P2C/cbGiY6t2yInyO5HbdUw1uboyJxbX1e29SeMcMS45/3WuEdU5h/jgx9NzNh3rmJh/Ld1PrKR7qEwLn9mPb5rejSZjPe39uGUuz/CzcvcP8r2itnXTOQgx/jmI9ZpByta+pLWe733/Vbc834rvv7QpzKkpU3MV+Moa5/N6JPM/t1YjiwadZeheMe7E7+RWIqF1cU4EAD+92knVm0bADfUFb95aTO+cMeHliusecnTv9XwW3XCPIHt/97aEjcr36jt9ndb4i7uX71/FS59Yg1a+0bGjylVYl4Zt3H9q94Cx0dWGm5UbKwy3nz0j9jf8FsxPDqG7zwW3w/Z9a0J9zIO3rip2zsUjuDtzT34+dPr0NKXWBc3NDqGyFg0yeTGiX/HUiy8pBqkMgksxj16Lek73mtJ8kmrHaf3I7c6xFRysGP89Y3N2oi7zSZ+8/wGnGdTmg0AVrm8eXUz0ty0faL9pbaSnispvkIBsk4qdZDveb8VFz/6qTbaYHmyk7eAWEkcFR/zu8Gtr5c+EV/72HzB+9hw4TTf3b61aeLu3/E6aZODbGe93YhIsnMVK4n26Ervj3vdYvbVOEok8s78vWbrx5ixfXy4tQ/Xv9qE59Z04of/XRuXc2lFtt8IxnwdMTSQXpugX+Zv1phe4PaCeu8HEzeK+Q4BsrGtGB+LP7KyHeGxaNzIbQzjoY6Mjjkeu1tfjNKWberBgzal38Y1WPSnzT2JAZvV/o2vmH8f+cEAHlvZjm39E8Gi8eLvhscMv3W7mwm7PFyvv9ctPSP4zmMczZEy6/2Yzrl5SeY8w5+xfnZT9zBe37A9YVtrOgZx4p0rcPkz6/FOcy/+8HJ8DerRsSi+cOeHONchKAPiB5BigXGeh4kTIoIpr7/XaBr7/esbm/HIx9uEp1g094zgT69tsg1g32jqiSv5mWof5eZ7xrlAqexFhZiHAmQdp/qnW3pG8NjKdsuGvaZjaHwSiRmnR20xfvnseoyORV39QF5a1413bQIWQOtob1rajOfWeFtmUyai6kv3GkZczJ1DwOEvI8YLl1UO8mAoggsf/AR36iMNxg481tE9saodX7jjQ9sV/YDko1k9w6MIecgDtcLsa3x+dVqbjuNnTydOkgSAXz+3AQDwo/+txTOru/CHV7QRt2QBsOjSUqJpbW3FUDiC0OiEiYM21Ttkdu8DxvY+5r3iQF4ggNDoGJ616As2G4JKq7zRGMbALfbPJz/pwOfv+BA//q/9jVAkGsVdLkbrjBfI3uHkyyZbpXnEtmHc1qhF8GkMVMybyQsE8Lel8bnFXgOjAmOOk+0IsvXrsX2NjI7hb282J01h+ssbm8DbB3HFi9ZLRZv7tqDpKp9v0Bqbu3DhQ5/gNy9sGM9XjnGxaZJql+lJaddgGGNRvdSZg2fGNIp3m3sxFI7EBepGXlzblVA9RsRNv9fJcdFo6r/xJz7pwM3LtlgGgelM0ovhfrJdatuPH0Cy3ojx+phSFYvsj48pQI5RWFho+975D6zC35Y244lV9iOCHYMTd22/eX49AIfaioZ/f9w2gJPvWoFPHQIuQMt3vvqljbjMJmABgJVtA3h0Zft4sJINOPnqhR7DY25zp2OcoBaKjMXNhDdi/JpxlCX2zxfWdmHT9uHx5V2Nn4lNkvnrm80IRaKOk3XMpeZa+0awQV8EpXswjFPv+Qhffyi9XHOzr4l1RuWydFNPSstaG4OcP722CZc9vTarctFe2RLGiXeuwDOrJwJLOz9lyu43jCBf8fx6zxfqvGAAt7+7Fbe9kxioGstSWeUox/ZllZL0lze0oGyF4YmOeSAwMjbxWNsJ4/bdDCZaDVBc/sx6DIUjcRf0WADywtoufOXej7GuczBuxNZ8PoMWO/c6ulVgiPbs4p+rX9po+XpsX0980oHHVrXbpjDFsLthM+//pqXNOOPej+PSOQCg0KDVfOTGSeaWI/EJfa+jFEvuXt6KK1/YGJcWFjtnazsG8fuXmxKqx3iNKQOBgLZATRpl9qJIPzC3ClCtqnh4TSFx+/nRFK8FcTeZNp+JfwJgFSE7a6QRZIWoqKiwfS92Gtc4jI4ZV397fWMPnlvTiSc/sS7DY/7RjYyOYZOhesXKtn68oteUjJUCcsrvimG82LmdKCMbJ19TJTKmTXKJHaPxZ/oU78SJd66w/PElq2KxzZSPZ+wkzLOcV7YN2Hpsvt6ec/8qXPTwpxgIRcZzwNzMAga0yihWE4bMvhr7wdhozVg0io9a+6XVvU1lWWtjgPwU78S7zX047V8fj99AxHhpnVaazGlpYCNbe0fw7OrOlDpdY4WAe1Zpk7CMM+RDNsMwUidhGk5ospUlrcgLuisHZ+Vv7LCMvxGnnETzo3LXKRYeIxa78mDvNfeZbhC1f1/zchO6h0Zxw+ub40bgw5Fo3LmzGsnsGhx1vUKeto2J6gJ2T4fsamq3D4TxlsNEPTNuMxMeXdmOrqHRhKc6VjWyrbAesTdpQWqP2d9p7o0Lrr/8r4/wz7e3oNWmqobX4Pbd5l6c+59V+OWzEwNKqfQN6Q41uB1B9irN7edTzXc2/t7t9mVMsbDaTbI9Uw6yQnR2xj+KXNMxiEc+3ub6ImgeiXEaxU22xUufWIOrXtTKyP36uQ34/B0fottQduqFtV2WkyWMHY655mUqbOkZdlwowQ1mX93itODH6vYB/PG1TePHaDWKYZ3nZbjgG+sgR7U0mvtNyykbtzFsMWpzw+vWjzjt6HFRKmsgFMFbm3oQGYtiU/cwvv0Yj0vhCY2OYUvPSIKvxo44tuTsk5904AdPrsHZ969MqRKHDKxGY7cPj+KalzfGvXb1S1ppsodd5nGf+59VuO7VTfjt8xvwvcdXW04+Wt0+iAsf/ATvG8qj3fleCz5/x4cJFQKM2AU8Mvv3jQ7LHbvBba3wm5dtwXcf45bHGHdD6XQ1M73l9hFy/Ahycr12aSZRmIJ5i/0bbxZGx6JxwZ/dY+gfPLnG+g0LVncMonswjKtf2ph0hNfMgx9twy+eXY8VLclXRgPs+0a7lAUzTgHya4Y8ZKvfqjngi0tb8xjxGM9530gE/1lhn4Pu9WZ0o/6UxFh1xPMIcjo5Fjpuc5C94nYTdjf3yXCjsXMwjGtf3oim7iGbOsjO31dgAJkC5BjV1dVxf1/8KMfNy7bguTVd46859T92j/UtcdkwhsNjWKqPLLxpqJhxzctN+Or9q5J2Go+vak9617ylZxi3vrMVp97zEU64/QN8YAgeznvgE3z7UZ7yZDUg3tetvSOuO1Gn66V5RM2yv7fYjV0Vi+89sTphMYRoNH5W9vColQfWx2J3sXczcPOLZ9bhF8+ux4MfbcNai1zE7zzGcd4Dq3DTR8O4Z/nE43Orc/G8oe2+sFb7dzgyhqbuobRzoFPFbhTQbpU+4wjny+u68cjH28Zff35NV8II6BtNPVi1bQB/fXNzgie/em49Nm0fHp9IGBmLjqfT3LPcPiXgiuc24PZ3EyszyBwBefJT9wvsWJEfDLiujf1p+yBeWNs9/vc977fi/a19cSO8Ts3FbIPba7LXFSyd8teNb5nb2Kftg3inOT5ANk7CFJETCgCn//vj8ZvTVFjZ5u4m1q4fMaaKONXILojLQY5/L9mTE3M3E1ff2KOPVgG9XYtVbZLexD4TX7Pqa7zuxu2xpDyCbJFiYW53V7+0Ec+v7caP/7c2pRxkr4uR+AEFyDp2JXOuc1nux8syvG6bhbFxWzWm2CjIYChiGSzf+GazY+1HAPj9y024/8M29AyPIhyJ4sd68GD8AVpN5FnbMYhT7lqBx/W87Nc3bscFD6zCJtOymzFfX93QrY/yNSVsyzMJvahFDqHF14x5W8YZ/wDwzOquuL+vebkprrKG1Qiybd9jU1FiKDyW9K45trDAm03bEy5eK1r6sEH39/3WQdy1vHW8I7MKYIyBeuwx+Am3f4ivPfSp5ROGaDSKF9Z24Yt3r3BVvN+OP766CW82bbd8z67DDo9p6SDm943+/e6ljbh52RZ0DoZxwxubce0rTbY1VN9s6sHvXtwY95rxJvbvy5px3G0fJNUFaL/Xf3/QlvDIPZtyp83kBQOunljEMPv4k/+txX8NQbqXi5nrEWRDm3UTyztNVDTqW5dsomgkGhf8iRjRA9IPplwH6nYBssHEHzlUkykwRDpOI/eWKRamq5dV+Ta33O8wYmwmnTJvMVK7EUpvv1b73NKb+PTX625kp1hYTXTdeWp81ZTYAFL3UGo1/7O4+xyHAmSdJ/h2/Om1TXhwRVvSUlVWeBlBdtuYjKN8Vg19eHQMLX0jOOmuFfj18xss+82mJCvzcZvJgcaROauBm18+ux6D4THc+GYzxqJR/Ob5DdjcM4Ib3ohPOxge1vb/2ErtYmscqXLC6XppbrRWfbyVx8aXtlp1UgZeNN1YPP5JBzpNucBRaBPwhsIRPLu6E795fgPCkbE4fcbR7ose/hS/eWFD3Da6BsP47QsbsNIUfAUDgYRHqf96Pz6oByYuSlYjCsbZ6+bHqlZBxAtru3HNy03oG4m4Kt5vx9OrO8erXABaWshL67rRNzJqe147BsL4wZNrcOvb8bWjrW50RkbHxnP0X9mwPSF/OcarprJVxt/Qw6YFNtxcSMyP3LO5f/+wpd9TgGzFLS6XxzX/1tyXedPydd3Ol3AKwIz7/NVz6x23E4qMJe1bYxifwsjGbQBnzPs9+76V+J9+I+M2xcI4obBneBQPmcrrbekZwVO8M26UPUbX4GhclY246iACbjSMTyuNN6RiRpAz8x0jD33s7ibA8wiyy2+kOmHbagTZ6XdtnWLhrFHETY9s8v0WkA28sr4b/1k9CMA539Y5xUL8CLJxlMOq8xkeHRsfIV7a1IMTd6lL+IyV5tGxKK55aSP2mlFpu2/jUs+jpgh5MBRBhyFY/KNhlN08ylZdNxUtfSNCV04zPjqORqOWx2j123Tq7IIB5/dfWtedcCHf2jOCc+5fhYbyQq3MEYAX11XGBexO5a6i0Sj+vqwZr23Yjtc2bMezFy4efy8QSHyk1ViRWBEkHBlDcX7QUrvxQpoXCOBvbzovkyurPOAd77XgwY+2YdfGsqQ5cY+ubMdWw6TC2HmMn40eRV4gkFBZwYrlW3rRPhDGmo5Bx0DI7UpxH27twxiAxdMrlBgBEUUkGrW94CWmWLgfAPjc7R8CAHaeWpr88zYTlaOIJl35y8jI6Fjcb8tppv+1rzRhRlURFk61rjksErepKcb+pa0/hD+/vhnHL6xzlccdjUbjbpZb+0L4x1vxN6XnPaDVNP7y7lMtt/G7Fzfi1lN3AWBfXi9VjGVMf/DkGnz7oBn4wi71QioeeH2kLyAFGf/71F2f6jlYdPlxt/2akeVbei1XmhWdUiayHKksaAQZ9qOoXvA6McMNxtn1diPIxpetJm9Y5SEu29SDVzZsdxwlNAbIr2/swbG3vj8+mrLdNDL1rGmUpdUwgfDSJ9fgq/evitueG5z6eqMXoUjUegQZwD3LW/DbFzaMd65Od7QFLtZqbjVVg4iNaLUZZl4PhCJxnpu9MfLsmq64xQmMfNw6EFf7GQCmVxYlfC4WcJpvoHj7QNwI8uhYFI+ZyhR+sm0Ap9/78fjEnLJCOffLsaV0P24dSBiFNxOJajd7MWLnzPhbGBkdi8u3dLru/fSpdbj+1U14fFWHYwcfu3gmCzF+9D9tMZSIy9rluUJkLOo+t9jl5/5rCB4+2Za8D97Qbf00LBpNHN0yp3oZGRkdi7tRc5ocBgDbLFYWlIGbEdhoNGqbg+xm4Y2bljYj3+VQ88et1iP7m3tGHFO70sGcfnCjflMv4rc2Fhf0ufDa5ef8wO1vMZWblp8+tc6yzJvz4kCJryXbc7Z6a4QCZMQ/cnLCKWiznsRljdt2Ycz9HbEYPXl9w3a8uNYQgLmc9JCsI77rvRa0Gy4Kt76zFWNRbTRlQ9dQ0oYd2/xQOILNfda+DIQieLNpe0o/YOPj0ZFR67zesWgUdy1vxWsbto+nEzjtycrfVIiMRV3/qK5/dZPj0p83GRYvaOkbwfItiWW7Yo/QzJ3Xdx5bjQ+2Tozmt1mUT7rk8dXo1FM8PtjaF3djI4LxFbPSWAf4sVUdGDaVORwZjcY9Tk6WLuOG2EiL2xJaociYEo8IRREZA+60mKhohdv+TVR7W90+mJB+caFDnfHh0bG40ehkcgvMK21Iwo1tWjNNvYrFY6s6XP8enfqmez/QJppZpWGkg9WCMSvb+oWswBlrIqHIGC58MHkd+mg0mrlSZJ5zkN19QUSZtzc3bse3H+UJA0QxtCcSFimNSfahQpk3SrEAsKgh/cdngyHxKRbGUdcBixFq89ry2yyCoNc2bMc5e0+Le60o37nDv+f9VlSXWDeNix7+FL/67I6O348Fa+ZHdzE2dQ/jB/9dg57hUZy3zzScvmfictROZd6Moz8ftvRbLjdr7D9inUQmfpCRaDT5MKRLjJ3bV++3Xsp1fAQ5ybFZBchGfpxC3n0y1nQMYuaUYrgYnHfknPtW4qpj543/PRIZixstMz/RSIWY13nBACIuhmfCkQxePLOAnzxl3z7M12q3Nw7p3DgZeeCjbXjP4ubRjn9/0AZWnzylI0YkGhVW6SJdwpExy5u4rsGwqxQLQEw/+MCKbTh7r2n44X/dl8JzQ5/Fsu7GydLpELNnZetA3GqS2YB57k4y3J7CVFIsAOAZPjHw9nvT8uJm8uzSE5PsWoWFQihABrCvQy6uW5KNIEfGop7WngfiOwvjY2c7rreouNG0fRhXvbgBwUAAPz18Ntr6Q1jRkrwAfveQfdBx93LnZWRjP0q7/Cvj6M5rG7ZbBshOQWbYMPrzW9OktxjGkY3YKGsmHumMjbmr6WpFKvrCkTGsbOvH66YJaWa2O5xPWXz38dWoKc1HTUlBWtvZPjyK3xtWIftgS5/nlJ1kxALkgMtLz0jEa5Gy3MXsg9vVxK1WsEuV9R7qRq/aNuA4OmpmODwmrNJFuvztzWa8tyWxhNtX7v0YtaXufmci+sHYqUs1ALNDRqpijFi/3O9y8SEgc0Gc1/7MrSyn0ohOrPCwSE5+XjBnl5rOaIDMGAsCuAnAHgBGAFzIORc/dOWRQCCAg2eV4/VN7huFmWQ/7GtfacLL67qx5/Ry19vss3jclAqv6LU5l23q8TSZ0I5kHYyXGrupxJJuip//17CK4YA+up+J3+OYw2SmZBgXBHHLPctb8YaLmyfjQjOZpGtwFPVl6S83bhzx+feHidU80mUwFME/394Ct9ep0OiYEjl0fuB2MpQq+X1D4UjWBMhOcxrcBn4fu6y37ITXwZ5sYHQsim39IcuypVZkxxm3xnUd5AzMhLNbeCaZxCz5STmS6T7qJADFnPMDAfwUwPUZ3r89Y8l/NM+s7rKtSmCXnxPjpXXdiAJ4f6v7INxt4Xi3iAiOAdhOLIuxoqXfdZC8pmMIWywed1ktrxyjaXvyfDRjneMPWvqwpWfEcj+iGQqPpVxey2nU3g43wTGQ2opKogJAEZNgZbN9eDTpZC0jm3tGlOjg/cDt8uDLXCyFnQ009474doPpBVHzKNzQNxKxrSqSzZz7n1Xoddk/D4XHsHl7dqVixHA70t6RZFK0CIIB6xS+ZHMMvMzb8otAJkdBGGN/BPA25/w+/e8tnPMdjJ9ZunRpdOHChRnTFOPX/1uJN7dmZrYyQajAfjMr45boJeLZsbrYtrICQRAEYU9+MID/nb9nxve7fPny95YsWbKPm89megS5EoBxyCvCGItL8xgdHUVTUxMGBgawdetWNDU1YWRkBE1NTejo6EB3dzeampowODiI5uZmbN68GcPDw2hqakJnZyc6OzvR1NSE4eFhbN68Gc3NzRgcHERTUxO6u7vR0dERt82tW7diYGAAh01P7THwmYvSz18m/OWgFM99OhTnZXyXnkklOHa7WEEusFGh4HhBjbc2/q3FVbbvnbOL/JrAfnIacz+BL5s5ZX6J3xJcc+Guud2miERO2qkc27dvz3i854VMB8i9ACqM++ecxz3vyM/Px+zZs1FWVobp06dj9uzZKCoqwuzZs1FXV4fq6mrMnj0bpaWlmDFjBmbOnIni4mLMnj0btbW1qK2txezZs1FcXIyZM2dixowZKC0txezZs1FdXY26urq4bU6fPh1lZWWYW12IR8/ZHdMrC1FaMGHL1PICx6oNZ+0/Fz8+bLble79csiMuPWRWWoaZOWTHKfjf+XviiHnVjp+7YN/pOGfvaZhdXZzyviqK4qO4P31uQcIM6uNYreM2vrNnecJ2YpQUZEcW4q+PXzT+79lTEv06aVE9dqrzdtFk9aU4bMcpqLQ49iuOmovHz1uMqeXpTV6zwqzzwFnxgc7Pjohvq24n9rjltlN3wV2n7ZLWNr60m/UCBQBwrqEiy1mLLSZ3Ath7hwo8ed4euP5zC/DMBXviKUmjFCplWFx04Cycsmu968+ftPdc7D8z8eZ/ankBzjpoJ9fl8KyYW+PcJ12433Tc8IWdUt+BzpRi71NsplcW4YJD2Pjf3z5oRtLvHLWgxvH9uTVaoHrhvtM96zFzgctt/OOUhfjG4QsdKxY59d3n7TPxO9tjmvt5M8n43bHzEn7fn1tYhy8fsBMuOXhm2ts/fqHz9UgG+1n8Tvzml0kqTfnNwvpSfP3g+ZgyZUrG4z0vZDpCeQPA8QDAGDsAwEcZ3r8tJSUlKC3Mw+2n7oIfGQLe605YgM/MmYLyQusgLxAAymzem1ZZ6GqFKK/kBwNJR+pKCoI4a3EjplckLi7hxMmLJi6i5uBpUWM5ik0dbllhHr5xwA5xHaqRWTWlKLC5mjaUZ37kFgBO232q5aIbgHXdyJL8oOeAYF5tCX6+ZEccumPijUys7Jmx2sXMKk3PMTtpF1s7z5JRmB/A5UfOAQBUl+Tjl5/dMa7MX1F+EL8/bqJkmugUq7xgAI0e2txpezQkvGYMOG47dWdcd8J8AMBn51ejylB+cI5NoFWcH0RhXhC7NZYjEAgknVD026PnJtVp115SYaGHEmOiCAbc1ucwfMfCt1hzmWVxI+mWZE0uLxDAzgJWrktWztKK2BH/7SSGXyzZEZ/fOXF1UjPJ5lv8/rh5+N2x83CKzY2flwDUrvymkYv23wE76kG53XULAApt6i/+9ui5WDx9YhzL3OenM+hSnB/El3efiiXzJ/rF2KTOonTrQQI4b5/0b0K8sqfAG4hU2GdGBWpM7eLgOVM8lTIEgANmZS7QV2VwIdMB8iMAhhljbwL4E4BLM7x/WwoLtWAtEAig3hC4xS72dhfZAOxHQvODAccOyo55tfaPxor0yDhZKbFAwj/cYbyo1FiMLlr5cMquU61LtQGYWV1qu0qd6JnhD5y1m6vPBYMB24DXSlNeMBDn96WHzMJ5+0xDXZnmz6KGMuzWGN9JlhZo5/2U3RJH7WLbMmq47nML8MslO+J7B8/Cnz63ALd/ObVR2O8dPAuHzq3Go+fsjvvP3A15wUBcQFaYF8ReO0x0hKInOcfah9uL/sFzEh/lG4OAGVXF2H1aBf51+iL84NDZccFVZZF1sDDDQ/B26cEzsf8s+3SCGH87ieELFku5p8IXdnE/kmvHrV/aGXd4aCPBQMDzVcnqJjy2icuXpD5ClWzCaOx3YTXSPL+2JG4E8qRF9l6eYfOEwYnYz3xBXSkO2XGK5UqkZsKRKC7czz4wqyrOxz4zKm1n+3t5krawfuLGYV51IXZvLMdiQ2Wk+bUl+KLBH7vlogHY1ifff1ZV3OJZxmvC746dh7+dyKy+5oqi/CCmlBTgJ4fPGX8tVmPaHIinQlUKTw1U5yt7NODr+++Q8LrXGw4vazlMFjIaIHPOxzjn3+CcH8Q5P5Bz/mkm9+9ET89EavSC2hKcubgRPz18YiTZrq0FAgGU2gTBecGA7eiyE04X0FiwaQzYnjx3DzxoExzGHqO5fdyYZwgep1qU53JT47eurADza0tw6m5T0dfbG3dhiI0c3HwyEx4gu+0c8wL2y5BYLQgQDMTfGBzHanH6no249vj5OI7V4mdHzMH1n1sQd0GPpenMqEq8yMc2ZfSyuqQAB+84BXnBABY1lo8H316oLS0YH9kztknjBc48qpZunc+60gL89cSJx+Gx6+rPj5zjqqRhMBCIG7n47IIaTCkpwDXHzcffTpq4ENeXFSIvGMAcw+iV+Vg+t7AOR8yrxukWo9Jmzt17Gr538Ewco/8+bkxy0S/ODwobnRIwUIaZU4oxvbIIPz18Nhorkj+JCdrEx1bnKNZ2rX7rxhHkZKkFdiQbcZ3Yb+L+84OBuEDK6fH2ntPK8eS5e9im4ojKlw9FxlDh0M8nC7LddIM3nshw40kMswztf9eaPFz3uQWOaVInOtxAOO3W2N8Zf2dTywtRmEYgaxUEx0aQi7Mk5c4zgha8SX33AZRbpPJ5LcXnpT70ZEHRFimeurqJ0aFAIICv7j0NR86fuAA4rfpUZvPDzjN15m5xCmZjj8WMq58W5gdRafpOrFM+YFYVbj91Z9x7xq622/zmARN3n2PRKB48azc8fPZuKC1M1O70k7vqmHmYU12Mq46Zh5tOXoiv7b8D6urq4kYjfnL4HDx74WLMqy31rbZoMJB48T9Zz888fc/E4CoQsB5xnlFVjEsPmYWp+hOHnx0xZ/w9u5umuTUl46PNTjcbqS42YoVVgLxrozYStdcOFZbfccuSBTVxKRWxTnlKSQHOdxFQBgPAjw+bjR8dNguPnLP7eD7/4h0qsMAi73vXxnL88rM74vZTdwGrL40LDvadWYmfHTHH1nsjp+3RgOMX1o37nCzIjN3singMKWoFOQA4cn4N7jptUdLPBYMBy3Sa3x83P+G1v+o3C8Y2H7v5nFE1ca5TzUNOtrhEbLtWNgUDgbj2bDcqCwD1ejBnZ3cwGMCNJ7K4JxapHFI4ErVMRzlt96m2qWdG3KzSN7W8IGF+QWW59ht2CoSc+hGne+N8mwA53ZsKq7SX2PEb3ztvn2mecuaNWOXOq0wyywOw/h04/TasGKAAOQEKkHWMI8hWODW2urJC1JTmY8fq4rj8rLxAwNUjOiPfO3hm3EXITKHLFAsjO1QVW+qvKs7Hw2fvhpN3nXgMNxYFyovyUV6UH9dh7agfV9DUYowX3X1nVuL/vrjzeP4boPkayzM1P6IWWbfTy41IXjCQcNH8xv474J6vLMJxCxMfo5tHkO0wPi0oKUgM0qqK8/H3UxaOPwVwM/LnBTuFxYYhy1iKzi+W7IiL9t8Bl3xmZlqPNoOBeO+N7YzVl+L8fZ0DhGAggPKifBy1oNb105aD50zBDlVFCAYCuHzJnPHXCzxcvc3n0+13Rdy42G3DbqTTK9MrE9tVns0IspWWmfpTCON7fzhhPo5aUBM3PyNVko4g6+fGyqW8YCBuXoe5Xztvn2n41Wd3xBVHzR1/z64PDkei2Km+dHwSndNnnQhFxixvei7Ybwfb1DMjbhZWMfbFPzpsFvacXo6DGrxfC4w4PT0y/j6Mv+/Yvg7bcUpK+7Tqa2L3B8b36ssK8Y0DZriaH2DmO5+ZmTAZWWWSjdgHLAZ8AO8B8mRMT0kGBcg64bBzQW2nAKkoP4i7vrwIN5280DJY+Pp+0109jtxnRgWOX1gXlwNtZrE+4hfbnt0y2VaPTs0zw6PRKMpNeZz1hkf7S+Zp+5hbUzw+quR12eBwOIwl82tw92mL8K0D42eEu11ec25NMU5aVO+YT+dl1nvQYkQ4EAhganmh5QhJMBBwNeqXrIMx7/OSg2fi4DlThMzYB2AbIRsvrrHOtrqkAF/cbSrKi7T8yBi3n7oLnjx3j7hUBifyAoHxmzYgvqMOBAL4yh4TAYJVNZN0R1ONE42cfqPJAnW7i8nOU0vjJmqJWEDMfJMZo6IoL6HqSCr8cslcHDg7fjvBQMDz0q7GUdE51SX40WGzx5+WAIB9otIEhXnx7QMAwklGTJ0uSsEAsPu0Cvzm6Lm487Rd4s75w2fvhtP3bMRn5kyJO34vp8zqs787dp7FqxNEo+7bxZ8/v1PCyKib1YCNv+GjFtTi2uMXID+q9Z9uH6XvbXpa5HQajL8HqwnD56aYbuR2BDl2w7r/rCrsM8PbU66p5YU4Yl5q6T/ZiPn3Y0a7niV+xm27+N2x83DQ7Cr81MVNxSPn7O5qm8lQZRFSCpB1Ghud7/STTaQozA8iLxgfSMUa6Jd2b8APD3Vf7s1pRC8WzOw8tQz3nr4Iv7G5w7bKfd15allcmSBjG/3j5xbgS7tNjSv9M6u6GP85c1f87aSF44HVT4+YM/54HgD2nO7cecV8bagoTHmk42dHzMG3DpyRkEYSY9fGsrhR62TkBexHioyvVxTlIQAtb9pNX2M3GXF826a/68sK8cvP7uh5xr5dsGcnsSjf8LjUQuOlh8zEOXs14p9f3Bk7VBWNt2W3BAIBnLFnA07Ztd6xcoBVMGwXLLrFePFwygM9bfeGuDkFZuwC5Bu+wPCdz0yUn0plhNFM0CYH3rztI+dVo6bU+6jOjClFuOKo+H7BLgfZjHE0Ndmgulsrqk031clGtsZHkG1uVgEtdWxaRVGcj+ab/XGdSfTF7cfiw/vMqMR8m4nTwYB2o+u2b9uloQxHL4gvReZmBNlq+7G+1e1EcPNv8/C51XF/z55SjGuP11Ju8i2uY8DEqHOZRfqdG5yCPbsnUemi4KrYcbh5umV16XH7YHBuTQl+fdRcy5jBTCpzqqyIKlLHggJkndbWVsf3S/LdNQzjj9HYrt1cWI2XTfNEox8eOithIl6dPnHJC8ZPG2s079pYjq/vv0NCkDelpCBuH7OmFOOPn9sJD561G647YX7SGpBOvp7osipArLO0vQh5/K0FHCbpGbnk4Jl44rw9UFdW6HoCyZXHzMUxO9XgSIs61V6Dq+tOWBCXHx4jGAhg34bEpwx2m3eapAcAFUX5OGuvaXETgLodlig1lueL7fPcfabjGwc414y1mkiSbsqCsb1OcyjFFggEcNjcaiyeXm75JMLtuRFxsc0LBCwDgADiLxw/PWIO6i0mypqDX6vtJLzmoorF53eui5sYmezcuJngGbD43BVHzXV82jKeg2xxJF5GQcc1eDhnXk/vXactwrzaUk83evWm+uepTpSN9a2nOjxZM2L+7U8pycetX9oZAFBZlIdbvrTz+ICHsc+PD5C1/6caKBl/Zz86bBamVRTiwv20Pq7YYgRZBF4GT7IRu3J8Mazm1ADubzL8GM0VFWjLhgJknbIy51E8t6V4gjZ33m4wtvHzTAXhj96p1nYEdfz7rnYy8c+D50xxrc1MZXE+dp9WkTSwcPL1GwfMwM0nM8vFOYzEfDw6xVnzVri9aMY6pwv33QGzpxTbLgoTY7+ZVfjBobMtA1GvXf7u08qtK5pEgYIC9yOLxg422eO6GF1Do7bveS2fddkRc3DYjlNw7E6JRfzTDTiNPifrdPOCAVxz/ILxC3IqiMhBzgta5xW63bQ5fSJxOxaPWwPJR23KCvPi+qxYqT67hX6sJtuVWvST5gvwntMr8J8z7ScNxzy28sNcS9jN6KtxO7Ea4YD1HAC7U2B33q0q0iSjwjTSnSQl25ZY32renh3mJ5PBgJZvfvupO+NO00TP/LgAeeK8xtL/kj0tc8NRC2px52mLsIM+58bt5EuvOG3KTcUbv7H7/cUI2KQAuo4/fBhhn+litDoboKxsnbw850bodiLThu6hiW2mMbqbCsFA8rq2mf4tOPmaFwxgXm1p0sAg5mNlcT5+eOgsXPfqprj3U7kBdnVBM2y4oaIQt+ijLSmTgvm29bct9NuNi+cFAziO1SIyFhVyYfN6GIfPq8bh86rx+Kr2hPfSDThrSwvw48NmozaFsnipIOL3EwwEcOXRc3Hpk2uSbtvt/r6021Q8+NE22++4WSjEPGp3xLxqFBcEweqtb3KtgtOKonwMhkMTLwQCGLPYs9ONtVO3aQ6c6lysBGn8XRw6txoPTq/AAyvaxifkunmeVFtWAHRYbFs/DnOAkmyhn5qS/PGb0ErDuvMHza7Cm009SfUAya9ZZsw37TGFO1gEK/mGtpAXCOD+M3dDODImpFaxHXZzGdLFsVqQgEBc9jV1TnUJ1nQM2b4fBBBI4bT86LBZ6BwMC19N1Yk9p5fjg639jqulZhMUIOv09vaiujrxsXiMU/dowCsbtifdjnHimci7YDectkcD7v2gDV90KI+T6QA5ma9uMObDWfV1qTwicuOD6CdPoppDFEBk1HlSqRnRS56nglWwL6Lk2WcFPVn4zkEz8Nc3mx0/I+KCGgxota7NBCyq3hj/LMoP2q7Cl0yVeZLeWYsbE9IVzAFyIBDAQbOn2G/U4gdSUZSHtv741+bVlKJrsDeJwnitgLvf6LRKLd/aadl283Yqi/NxgeEpgtFju+b4nYNmYKlF4BqLS4zNYsn8anx5d+eRyUsOnoVfPbceAPDdz8zEX97YjNP3bMTsKcV4s8ndArNe+9Yii/Nrh/HaFdBL66WyMiGgrUK5l03ZRjs9bkrfucXpJ5vurznVah5e+PLuU/Hcmi7b9+2qWCQrp3jUgsQneiUFQQyFxS8YcubiRkwZ68cX9pmPUCSaclvKNGqozAD19c41F3eqK/X8iN/rXbD547ELltutnLP3NNx8MnN+hJzhoubJfHWDMbCyCoZTSfh3c25E52a5y3xOThRRHLlA89VY8krkqb362Hm25QZT3Y/lJD0fHu/Z8TkXywqXWZTv84r9okOJv3Vjm3nknN3HJ1FZfdfq3zGCQW3FR0B7XH7O3tMSgvQCjzMmrX4e5sf9AWjzJ/aa5n4iaky/26cdB86uwrxa++DLUw6yzYfrygotU4RiHzd+7SeHz/GU99pYUYTfHTsfuzWWexpUSaVv/YyxuofDroz9YzjVHJBxovjuZ2bGTQBPhpvUGbeIHI02c5khZUc0n11Qg+8fMguzq0scR+4DiK/KFEtZNFaL+a5horETVnXRRTBrShGOWrTD+M2WKqijVDJdXfZ3aDGsatua+YW+BKvbleucuPrYeWgoL8TvbS6KZoIBLWXBKbXDblETWbjxNRnJLhopjSC76DNF/5CFjSBHgXklI7jpJIZrjl8wXurvMNOs9HTYe0Ylrj52ot2lWrTfiNV5FDEiK4qE0VuLz5y5OP2cRbsLdgDJ0wtsq6/Y/Nu4z88uqMHlR87BP21Shdzmp8ew+tlZlQesLi3AyXPd1/yO+XPJwTMx06EmvFuSHVUy72JY3YjHzoeoeM78G1k8vRyXHGwd3HjtW6dVFuGbhlKbbs+2Xb36G09i+OYBO4wvfGRHKt4ki8nvPG0X13V7nRdk8qIqERFVbew4eE4VjtVvKhxHwU0jyBcfpJ1j48Ifbm7+geSpQakTEBILZBpKsdCxWmUqFQ7ZcQqePG8Py6tHMOA869o8wrj7tArc/ZXkq2R5YcmCGry3pS/pRB9RuPE12U8yWS53KmfOqV+79OCZ+KClP2mFDpH7TIX5+iPLnx85Bx+29HuuF5oMo94j59Xg4Y+1HOJUD8NqEl0WxceumFJSgLMWN+Ke952r3jhhXhEtRrIUCyeM37O6aMdmuh/qcBNlVybNDquf9kmL6tE9FMbL67frWvTPeviVxsr1zZpSjFtP3QVXPLceb7jMy7VEYhAT27LXPmjP6eWoKs5PWMnS3Nddc/wC221Y9a1WOm48ieHdzb04ZqdadA9NpGa5HVkN2Tyq36muFDvVlWLZJuf0GS/ZEgfMqsT7W/ux+zTnoLu+rND1SqyOfUyGn6h6wRgPOMYNgfh2EzuvvcPxE61vPpnhm49wx33KGm0PBsTFWJmERpB1RKQCxCjMC1rOUv/PmbtZfHqCTPxWC/OCuHzJjlgyX0zeZjLc+Gr82fzw0FkJi5/IyOUOOoR5xy2sw8+OmON5kmUyRKVYAPG+lhbm4cDZVUIm4BmJS4kQIN2qEozMx5/pYictnWaxZH61/cRLWKVYuCPpTaaDz5cePBOH7jgFh3jOp0y84BXmB3HZkTsmvF5T7b6/MQdHn9Gr7cxNsVxX0hFkFznIgHWQEjuVXq/9JQV5uP/MXeOWpwe8Tex2e83aqa4UZyxuRF4wfkEJtz+94SQrnsZGLO3wcnN0xVFz8fDZuzlWpLn4wBnIDyYuQGOH03GK6H1kdWHG7To5GDSlWMRsMS/ENa+2NC7FJpMEIDbGyhQUIOu0tbVJ34eQMm2K4dXXo3eqxVWmlauSXTNSmdDhR1wmap/RaGbaq1Fv0OZ1L5QqUvsyGek8VnUKpIIWOciiOgWn39BxC+tw+ZIdPd+IWh2LOWaJ/dnRaVECwoLplYUJ/i6ZX40/HD8f13/OfjTViWSHZbxxdbqJtTp16bSFdG8OU+kDjF6km2IRY9aUYhzkEHh56Z4DgUDSG/3Y4NMvP5t4I+aVbH6CZdTmNPpqTrGIpa3F0p2Mc0mS1Zd2upm57oTUfn+axkBGrlmioQBZp7zc+ZGODIwr0gFZ/bQnZdz4mnyEx/kTm7cPe1CkXeiyuWNMRhSZaa9xF9O4C6sxoHCPsUbuUQtqcODsKsu6udlOOm0naayQEGCKaagyRuqtjsVuPzUV7tqrVRwQCASwx/SKjCwu4GSTVZAioxuxqs9sRSp9gHkpeFf7SVKHF3Bu16KfrMd+f4saysfn/DihaIZFHE4eJqZYaP//3iGzcOpuU+Mm3l2w7w6YXlmE73usanTfGbsmTXtxIgB/Yqx0Ue/qlCMcPncKfn5E+nfAuY55ZMLYT8R+5F/bP5WFHzLfM6YapJy2+1RMM1w0M5XJFTcqIeBKYpyJ/aPDZuOKo+ZKneSSLnbK0pHsNBIUDAQSfHb9eDqJJhmTIa2U2aUI1Jfl46t7T8MPD818ucGkbcylNdbBu3c9yZjmMkBOhbhAKslnrzl+Pg6bOwVn7ultYSAzonNPjZane5Mt4gZUVg9m7AucVls0V7GItffa0gJ8bf8dMNWw8mlDRSHu+PIu45P/3PCZ2VWoSbdWcvZ2845QgKzT39+f9DMiO8OAVU0nVVuRA258dcJqVnyMY3aqwf1n7IrP28zQnV9rnbMYgD8jyKnu8oL9dsAdX95l4oVoNG1f3WA3gmzEy6WvVECJtGzAKX89GU6xglUwPBgSU5NUSnt3yMk109/fjzMXN+Joi1JpSTaZNslTLKz/bcaoraYkH9MrC8cr3aRSatJWj8sLTSp9QNDtwQJYPL0CPz9yx6SpgckQX09+Qnh+MJBy6g2gzhXXMQc5ED/HIJ1VumXNowsG0o8F/IACZJ2GhswvOZkwIUeVX6sH0vXV/IM1jq4EAgFUlxbYXlAK84K4W19CtaE8flQmk14fMa8aAHD8Qvd37WbMx5iJ9hpXGUHA9koL8/DNA3ZwXZPTD/550sTjSNuSammY4VS7fyya6HPviP2S314QsSCLGaug0DyCHPPQj/41RrIbs7iY0aVN/zp9V/zjlJ3HgzWRgYXbmxkrT2tKnEf68gQ/FXKDwDU/ACSeo3m1pThn72lxy4i75Yj51WnrcZv646UONGDOQXb4YEKKRerntdhiNN7r5qwC9AACvvYBqUIBsk57e+IyuGZEdoJFecGEhpfplfcygRtfnTBbvvu0Clx6yCzceBJz8d0oGioK8e/Td8Wtptqvmaye8KPDZuOmkxhOWiRmFm8U6fvqBqvHdtq/U9/mybtOdV2T0w8CQ9vHH9lW2uReptNynB43j41FE4Ij80x0O5JpktG1WE/Ss96RVXuNLVxizLmVMYJ1+LxqHDGvGj85fHbSzzpO0jOIywvGL3ggUrbbvsno6Q1f2AmfmV2F7x3ifPNpTLXJVA/45d3FLitsNdHsrMWNjiUMrfjveXugviz1dJY/f34nAMCR82vw2QU1+LlDgL7n9HLsP8tb2VBjW/RSxSKd3/qMquKEKlJesVs/IBPXLNFQgKzj5rHWjjX2j/u9UloYjFt1akZVEc7fZ7qw7WcLbnx1GmGwCiiOY7W2tWStqC0rSCi7l8lbkfxgAPPrSoXl20ajcgvUx4jPQZ54Pfdu4yYIBAK45vj5WNRQhiuPmWf7mVRxutBFoki4+4ilCRlXTEyFTOV6my/OsT+t9n/VMfNwzXHz8QXJN0z5wQB+dsQc29KWbq1xCt4XT69AcX4QB+sl6dLBbYBj9HTnqWX41VFzkwZ8btKmRLGwvhS3nbozvrSb4ADZ44qPttsxVcvwElgeMa8au+g3ePnBAH582GwcNrc6rm88Z6+J3O2pZYVpr6zr9DmR80XO33eaeQ/W+7X5vt0odDbPN7GDFgrRqalJXqfzmJ1qceObza4LlDtRWpCH/GAAD5+9G/KCAVer9KmIG1+dlhVNx2qni8We0yvw1uZe1JelOfnAJ9z4mi7xQbF6nVsq1NTUYEZpKf6kjw5ZkVYVC4f2HBmLJrj8syPm4PFV7Th5V7FBhgjMhzKjqsj2ImjVXksL87B4hwqs7xqSoM4L7p6OOHVFZYV5eOSc3YWM1LsNcFLpA4zblr1uQyCgjUiKJlmpMj8JBibSqM7aaxruWq4tKBQIeE9zcn+jJC7FQsT3rZbFDgYyc80SDY0g67gZ/s8LBoQ9Jo/VhC0vys/Z4Bhw56vTDN10+vCLD7QuYF9aEMRJi+rxsyPm4K8nJk/VyDYyl2JhHTgoOBDgGje+pjMSYmzrVx87D6caRtci0cQAeWp5IS7cbwfUpjuLXALmn615VThgoq04+RobiQPETnZLBaczm+xmPc9hKXAvpJJikY3ICsBlLYcsIu1O7LLW7r6QkGKRZlSX8BTIo26rABnI/vZqBQXIOpWVYpcVTkZVce4GxUbc+BpxmKifaomgPaaVo9oUVPzsiNk4bMcp+OyCGuQFAzhiXnX65Wt8IhPt1f5xbO5GyG58FXV93ntGZVyJwrGxaMo3H348vjQHs04/VSdfd55qCJB9iI/jrHO0MTPi3LavdPsA9Rb+1ciXNILs5Sdk91Gnc+dmlcQzF0+kZRg/XuRwzAkpFmn2z+Yg32lrX91bS8cwTpAszk+MbQIIZDzGEgEFyDqRiLvJMOnyzQN2wN47VOAwjxMKVMWNr04pFiI78SPm1eDnS3YUviRzpogt1byooSwj7VV0FQsVcONrOl44jUJGomqnsjjmV2eof02F+PjYaZKefC2A+0AtVU/3mFaOOdXFqHCxAEg6yLIrlf77L1/YKWkVCSfbT91tKk7fI3kVBqcbVTcBsrHuv/HT156wAKy+FLOmJKasmHeZ7lMYLwMAZy5uxLMXLsY+hol9RfmJGwgEsrsPsEPNSEECAwMDGdnPybtOxdXHzUehokGaV9z4OuYQNaR6UcrFNIA7vrwLrjl+PvaZUZmx9hrDeBpy0Npx3Pia3iQ9+wYdSWME2Q8SfpsOv1W37dWPUU2j5VUONX/9WKDHiVT7gGuPn4+/n7Iwo5V8RJJKisXCqWWOy2EDzr/rXRrKcN6+0w2ftf7cMXqd71hpz/HPI+AqB9muctDOU8vw1xMZFtYnTtYVfRZTaRfGr1iPIGcuxhIJTdLTaWx0t1qQml2Kf7jxNVltWEKjuqQA1XqdU7ftVQbGzjCbJ8ykghtfZU3SG4umkWKR2tfSwvzbdAr+/WyvSTGY960D7VflzFRX5PYnlaqngYD45xQ7VBYJ3qI9qfY5ya4lTls1P+W0++zX9p+O/WZWJizLXFIYdNVv2M37iGE1Ci36RsdL9YyJf0/8YVXFIhgIZHcfYMPkGMZ0QWtrq6vPKXrT7RtufHWapJfqZSnXT5Pb9ioM02m49OCZ2LWhDF/YWcyk1WzBja/pTdKzfy9isVCICMyjWeJI/tuMHY/r9urzDXGdQ+Ub0Usm2+E24Ml4H+DAWYsb8aXdpuImF/Xp0yXV9QKcrzPON75O82SMFOYFse/MyvFawJcvmYM9p5fjzD0bXaVYxAWdFu9bjUInpliIxY3bxt+G5Qh/ILvaq1toBFmnoEDNyVrZjhtfI04pFiLF5BCZbq/mFIvjFtbhuIXZu+BHqrjxNa0R5KQpFuJDZLsl10VjPLLfHD0Xf35tEy5fsiMA9+11zIdfvFvHsy0HOZuuWaWFefj6/vGj79KqWKSYnph0BNnB+FTrkB+6YzUO3VG7Qe0YCCf9vHGCndWNklWFitinjtmpBm39ITSWp774SaoU5wex38xKlOQnLoAGaBqzqb26hQJknaoq5/ykGLk+MikaN77KSbHI7TPltr2KImqOkHMUN76mc/jJ6iAvmVeNJz/pwOLpiSXTvPKtA2fghbVdOEHSjUxCCrLhhQNmVeHfZ+w6HnRkur16we1NSbblIGezp4C8kn2plnlLZQT5iqPmYl5tCaaag84UbmTdxPXJKqpYtY1Y+/3BoclXikwJF4caCATGF1a6/tUmy01ke3u1glIsdDo6Otx9kHIsPOHGV6uOq0HvkKxqqxIe2qsE7Opc5gJufE0n5y9ZDvKixnL86/RF+N2x1qv4eeGkRfX464lsvOa6aJKNEBoDT9ftNYsfGWVqBNlt/OdnH+AnblIVrEg2MX6exZOW4oJgYnCM1G6S3eiOL9dm9b6719LB3My9bt4uiFexvdIIso6Kdzcq4GoE2WKY+KaTGTZ0DWO3xjKLbyQn1+9jMt1eK4rycMnBM7GipR9776BePUu3uBpBTqNtWd0MHrLjFLy2YTuOWqDNgE+2XHC2YD4UpwAym/tX1ykWGauDnCMjyFmzUIj2+f1mVuKwuVMQGQPO3ce8nLI2CfqHh85CR08/7viwy/BNMXivYmH1vloXtvxgAKNjUcyaUgSMZHd7tYICZJ1QKOS3hJzEja9WaRQVRfkJM4G9MNuiXmQukan2+veTF6I/NIrK4nycsLBO2uP6bMGNr16uz/vPrERrXwhN24cBWLf1y4+cg8HwGMokjfRmCqcA0m17zeIB5IzxuZ3r8MQnHTh6gfPSvNl+zZJR2eKLu9ajMMUnWHnBAH5+5I6Onzl6p1ps2xbBHR9qf4scnfU6gmxVa8RqC6KD5iLTSLvXeRHGjz941m4YDEcwpaQA2/qyu71akbvPSj0yNDTkt4ScJNO+/t8XF+L0PRrGV/jJVTLl69zaEuw+bfKkubjx1UuRrJrSAtzypZ2dtxcIpB0c+zGw5CWYddtefV9Jz4FMlZzcsaYEj311d/zg0FmOn8vWa9bfT16Izy2sw8UHzRC+7YsO8L7N6ZXensjE+2rdOFJKsXDR0AJJR5CdvyOCmtICnGcxwu4W40TD0sK88cow2dpenRASIDPGTmaM3Wv4+wDG2FuMsTcYY78yvP4rxtjbjLE3GWP7idi3KKgOshzc+HrCQu3R8ud3Tn90ck51Cc7bd7q0vMtsQcWakioguw5ysolCKmEueza3xr5aRja3V7enszCDNb9LCvKSjtxlq6dza0vw3YNnYkqJv1ULbj6Z4cRd6nGBYYEPNxh9baywDq5TCUqtKlCYMQbRltUgrPJ7vUtJyul7ptG2bARla3t1Iu0UC8bYDQCOAfCB4eW/A/gigPUA/ssY20t//TAA+wOYCeAhAPumu39RtLa2Yvbs5LNAKUD2hhtfv3ngDBw2txqLGlLLN56MuG2vhDfc+OrlkaPsGqV+YjyWiw+cgRMcbnDdttds9uebB87Atv4NOHNxdlzoqQ9wZl5tKS4+yHt5ttbWVtz55V3QPTRqOUEvVdyMIBsDDOt0CqvXsisqsVOjYnsVkYP8JoBHAVwEAIyxSgBFnPN1+t/PAFgCYATAs5zzKIBNjLF8xlg957zduLHR0VE0NTWhrq4OPT09CIfDaGxsRGtrK8rKypCXl4fe3l7U19ejq6sL0WgU9fX1aGtrQ3m5lrPa39+PhoYGtLe3IxAIoKamBu3t7aisrEQkEsHAwMD4NgsKClBVVYX+/n5s374doVAIQ0ND4+8XFhaioqICnZ2dqK6uTlgusa2tDSUlJeju7kZtbS36+voQCoXGv19SUoLCwkL09PRk/Jg6OjpQVVWV9JiGhoYwPDw8/n5xcbGwYwKA5ubmpMdUFwggPJKHrQocUzacp/7+fmp7Eo5pdHQUHR0djsfU09PjunPs79PO03j/FomgpaVF+DEZH19u3rw5I+cpEomM73O/mlFs2bzJ9piGhobQ1NRke0wxotEompqaMtr2Ttu1Fm9v2o4vLKhAb2+vY9v7/ZGN6OnpwcBAge+/p/7+frS0tGT170nEeQqNTOSuNjU1CT+mGGORCJqamhAOh1Ee7kfZcD+Gh/PijinGyPBIXHt2c0x5xcmD9S3NzeP/bmlpQWPpDnHHNNjXm/CdTZuaUFFeLvw8xRgYGIiLjYz09/cntL0xQy6S8fdk7gP8anteCLhdHYgxdgGAS00vn8c5f4cxdjiAb3DOv8IYmwHgIc75/vr3zgcwF8AwgE7O+c36668COJ9zvta4waVLl0YXLlzo6SBE0N/fP264E7e/uxX//kC74F1x1FwcmGR998mOW18Jb5CvcnDj63NrOvGHVza52t7xC2vxvYNn4eh/vg9AW7TjppPF92/3LG/BXcu1i9qzFy4Wvn0rvvf4aqzaNuBqn8l8jflTVZyPB87aTZxIl0SjchZpkclk6QMue3ot3m3uAyCnbcfa3pHzqvHTI+Y4+hr77LE71eL7SXLEzQyFIzjxzhWOn7n/jF1x2r0fAwBuO3VnzKiKn2z+4Efb8H9vbYl77ZkL9pTSds2+mF8PBoCnL0g8H397czMeW6WVdDOer2xpr8uXL39vyZIl+7j5rOsRZM75rQBudfHRXgDGWT0VALYDCNm8nhV0dna6OnnGZkjBcXLc+kp4g3yVgxtfh8Iu150FcNjc+GWeszmFwCteyp65ba+ZWs7ZjGrBMUB9gCxk+eqqfnOSFAurGvTZ1nZ3n1aBx1Z1oLIofh6Qiu1VeBULznkvgBBjbB5jLAAtP/k1AG8AOIYxFmSMzQIQ5JxnTeXo6urq5B8iPEO+yoF8lYMbXwdCkbi/z3LISTWviJdDc/Q8HQu1V/FMFk9PXjQVgLaUskxicaYsX13lIBuwCnyL8jMfDNvJtlNy8JwqXHXMPNzyxfjqPSq2V1ll3r4B4F8A3gbwPuf8Lc75e9AC5aXQJuhdLGnfKeG2BEm23a1lOyqWdlEB8lUObnw1jyCf46GkoLQRUh/6JSll3lKTMimZLH3AvjMrcf+Zu+L7h3hLaUgVV6UeU6li4W0A2TIALVJgFdNAIIB9Z1aiujQ+31fF9ipkoRDO+csAXjb8vQzAARaf+zWAX4vYp2iGh4ddfa66hNZW8YJbXwlvkK9ycOPr4XOrcd+HbUk/Z4X75IzcgtqreCaTp9USS8YdNLsKbzb14OidtHKjsnx1M7hm/IzVx61SLGQj6tZbxfZK0Z6O2xp9x7FabNo+jANnUf6xG1SsfagC5Ksc3Pg6t7YEJy+qxyMr25N+NsYlB8/EDa9vxnckLJ7gF14Gw6m9ioc8FcMvP7sjugdHUVumBeF++ho/gmyRYpGX/SPIdqjYXtV1WzDGsiZOFOQF8e2DZmLvGZWSFeUGbn0lvEG+ysGtrxVF3haiOWFhHZ46f09pqxL6kfjlZZKeW19zKUdbNtQHiCEYCIwHx4A7XzOR0WS1j3m19ovxZFRICqjYXilA1ikuLk7+IcIz5KscyFc5yPTV1Sx2hfASzCbz9eRFWj3aL+02NR1JkwrqA+TgxldZv+RkS02XF+XjT59fIGnvclGxvVKKhU5JiQ93ZpMA8lUO5KscyFc5JPP1ogN2wHELazF7inoXUb+gtioHmb7WlRagYzBs+74xJg7ahOEVRdkRtnktWKBie6URZJ3u7m6/JeQk5KscyFc5qOqrH2PT0yuLXH82ma/BQABzqkuoSpAHVG2r2Y5MX+/6yiI8fu4etu8nm6Rn5gceFytJBVG/SBXba3bcimQBtbW1fkvISchXOZCvcnDrK6XKAhcfNAMVRXn4/M71ST9L7VU85KkcnHwNQPvt71SXfNloK/KDAeS7TLVy86ljdlKnDajYXmkEWaevr89vCTkJ+SoH8lUO5Kt7qksKcMnBszDXxcQh8lU85KkcnHy987Rd8MNDZ42XhBNNXBWLLHmYIkqGiu2VRpB1QqGQ3xJyEvJVDuSrHET6+qvP7ihsW6pD7VU85KkcnHxtrChCY4X71CIvFOXFh6K5lm6kYnulEWQdFWv0qQD5KgfyVQ4ifd19WrmwbSUj26+l1F7FQ57KwS9fHzx79/gqFr6oSERU36Jie6UAWUfFGn0qQL7KgXyVg1tfd55aBgCu8wknO9RexUOeysEvX83LSNsGplkyAcJrz6die6UUCx0VS5CoAPkqB/JVDm593WdGJa4+dh7m1Nh/nkLnCai9ioc8lYOfvhrTKoJZ8lioIChmHFXF9kojyDqFhYV+S8hJyFc5kK9y8OLr3jMqUVtakPyDBLVXCZCncvDT14DNv/3gR4fNwvzaEpy1l5jUCBXbKwXIOj09PX5LyEnIVzmQr3IQ6WuuTbJJB2qv4iFP5eCnr9lUxeKoBbW46eSFqBE0CKBie6UAWaeurs5vCTkJ+SoH8lUOqfrqdyjs98U0GdRexUOeysFXX+OWms7yH7VHVGyvFCDrqHh3owLkqxzIVzmo6mvA9xDdGVV9zWbIUzlkzQiyzWfy87L7t26Hiu2VJunphMP266MTqUO+yoF8lUOqvgYCQDRLZpdnI9RexUOeysEPX2dWabWV3Sw1Pb2yCCfuUoeGcp9zej3G6Sq2VwqQdVSs0acC5KscyFc5kK9yIF/FQ57KIRO+7jGtHB+29GPXhjLsNq0cJyzU0g/cTtK7+KCZUvXJQMX2SikWOirW6FMB8lUO5KscRPqq5oNQOVB7FQ95KodM+PqLJTvi2wfNwG+Onovz9pmOqRajwdlS5k0UKrZXGkHWKSsr81tCTkK+yoF8lYOqvmb7pVRVX7MZ8lQOmfC1sjgfX9ilXvp+sgkV2yuNIOvk5eX5LSEnIV/lQL7KQaSvOTYAlBbUXsVDnsrBT1+NfUauLdKpYnulAFmnt7fXbwk5CfkqB/JVDuSrHMhX8ZCncvDT1/g6yLkVIavYXinFQqe+fnI97sgU5KscyFc5pOqr75cy3wU4Q+1VPOSpHPz0NRAI4M7TdvFt/zJRsb3SCLJOV1eX3xJyEvJVDuSrHMhXOZCv4iFP5eC3r9MqijCtoshXDW7wek/ut6+pQAGyTpSKmEqBfJUD+SoHkb7m2iPSdKD2Kh7yVA7kqxxU9JUCZB0Vh/9VgHyVA/kqB/JVDuSreMhTOZCvclDRVwqQddra2vyWkJOQr3IgX+Ug0tdMjh9n+1g1tVfxkKdyIF+dOXEXbVGTkxd5C3hV9JUm6emUl5f7LSEnIV/lQL7KIVVfA7TWtCPUXsVDnsqBfHXmGwfMwFELajGvtsTT91T0lQJkgiAICWT7qC5BEIRX8oIB7FRf6reMjEApFjr9/f1+S8hJyFc5kK9ySNVXv4Nhv/efDGqv4iFP5UC+ykFFXylA1mloaPBbQk5CvsqBfJWDUF8zGLWyqdm9jCu1V/GQp3IgX+Wgoq8UIOu0t7f7LSEnIV/lQL7KQVVfd2ssxzXHzce9py/yW4olqvqazZCnciBf5aCir2nlIDPGqgDcA6ASQCGA73POlzLGDgBwA4BRAM9yzq/QP/8rACfor3+Pc/52OvsXCdUslQP5KgfyVQ4p+2rxtUyfocU7VGR4j+6h9ioe8lQO5KscVPQ13Ul63wfwAuf8z4wxBuDfAPYC8HcAXwSwHsB/GWN76Z8/DMD+AGYCeAjAvmnuXxg1NTV+S8hJyFc5kK9yIF/lQL6KhzyVA/kqBxV9TTfF4k8A/qH/Ox/AMGOsEkAR53wd5zwK4BkASwAcDG00Oco53wQgnzGWNZWjVRz+VwHyVQ7kqxzIVzmQr+IhT+VAvspBRV9djyAzxi4AcKnp5fM45+8wxhqhpVp8D1q6Ra/hM30A5gIYBtBper0KQJxro6OjaGpqQl1dHXp6ehAOh9HY2IjW1laUlZUhLy8Pvb29qK+vR1dXF6LRKOrr69HW1jZeZ6+/vx8NDQ1ob29HIBBATU0N2tvbUVlZiUgkgoGBgfFtFhQUoKqqCsPDw9i+fTtCoRCGhobG3y8sLERFRQU6OztRXV2NoaEhDA8Pj79fXFyMkpISdHd3o7a2Fn19fQiFQuPvl5SUoLCwED09PRk/po6ODlRVVfl6TEVFRWhubs6pY8qG8zQ8PIy2tracOqZsOE/BYBAdHR2ej8nq4WEoFMLWbW2+H1M2nKdwOIympqacOia/z9Pw8DBaWlpy6piy4TwFAgF0dnbm1DFlw3mKxXZ+H5MXAumuj80Y2w3AfQB+yDl/Sh9BXsY530V//xIABQBCAIo559fqr78P4CjOeYdxe0uXLo0uXLgwLU2p0NHRgbq6uozvN9chX+VAvsohVV8/d/sHCEXi+9L/nb8n8oPq5d3JgNqreMhTOZCvcsgWX5cvX/7ekiVL9nHz2bRSLBhjuwB4AMAZnPOnAIBz3gsgxBibxxgLADgGwGsA3gBwDGMsyBibBSBoDo79ZGBgwG8JOQn5KgfyVQ6p+moVBlNoPAG1V/GQp3IgX+Wgoq/pTtK7GkAxgBu0OXro4ZyfCOAbAP4FIA9a3vFbAMAYew3AUmiB+cVp7lsojY2NfkvISchXOZCvciBf5UC+ioc8lQP5KgcVfU0rQNaDYavXlwE4wOL1XwP4dTr7lEVraytmz57tt4ycg3yVA/kqB/JVDuSreMhTOZCvclDRV1ooRMdr8jbhDvJVDuSrHFL2VcEan5mE2qt4yFM5kK9yUNFXCpB1qqqq/JaQk5CvciBf5SDSV4qZJ6D2Kh7yVA7kqxxU9JUCZJ2OjqyZL5hTkK9yIF/lkKqvFAs7Q+1VPOSpHMhXOajoKwXIOire3agA+SoH8lUOQkeQhW1Jfai9ioc8lQP5KgcVfaUAWScUCvktISchX+VAvsqBfJUD+Soe8lQO5KscVPSVAmSdoaEhvyXkJOSrHMhXOaTq69E71SS8FqAk5HGovYqHPJUD+SoHFX2lAFlHxRp9KkC+yoF8lUOqvn59/x0EK8ktqL2KhzyVA/kqBxV9pQBZp7W11W8JOQn5KgfyVQ6p+lqYF0RRPnWndlB7FQ95KgfyVQ4q+ko9uk5hYaHfEnIS8lUO5Ksc0vGVEirsofYqHvJUDuSrHFT0lQJknYqKCr8l5CTkqxzIVzmk42uQImRbqL2KhzyVA/kqBxV9pQBZp7Oz028JOQn5KgfyVQ7kqxzIV/GQp3IgX+Wgoq8UIOtUV1f7LSEnIV/lQL7KIR1fqWqFPdRexUOeyoF8lYOKvlKArKNiCRIVIF/lQL7KIR1fKTy2h9qreMhTOZCvclDRVwqQdYaHh/2WkJOQr3IgX+WQjq80gGwPtVfxkKdyIF/loKKvFCDrqFijTwXIVzmQr3JIx1eKj+2h9ioe8lQO5KscVPSVAmQdFWv0qQD5KgfyVQ7p+Eo5yPZQexUPeSoH8lUOKvpKAbJOcXGx3xJyEvJVDuSrHNLxlcJje6i9ioc8lQP5KgcVfaUAWaekpMRvCTkJ+SoH8lUO6fhKA8j2UHsVD3kqB/JVDir6SgGyTnd3t98SchLyVQ7kqxzS8ZUCZHuovYqHPJUD+SoHFX2lAFmntrbWbwk5CfkqB/JVDun4etruDQKV5BbUXsVDnsqBfJWDir5SgKzT19fnt4SchHyVA/kqh3R8PWlRPa44aq5ANbkDtVfxkKdyIF/loKKvFCDrhEIhvyXkJOSrHMhXOaTjayAQwPTKQoFqcgdqr+IhT+VAvspBRV8pQNZRsUafCpCvciBf5UC+yoF8FQ95KgfyVQ4q+koBso6KNfpUgHyVA/kqB/JVDuSreMhTOZCvclDRVwqQdVQsQaIC5KscyFc5kK9yIF/FQ57KgXyVg4q+UoCsU1hIuYMyIF/lQL7KIV1fA7RciCXUXsVDnsqBfJWDir5SgKzT09Pjt4SchHyVA/kqB/JVDuSreMhTOZCvclDRVwqQderq6vyWkJOQr3IgX+VAvsqBfBUPeSoH8lUOKvpKAbKOinc3KkC+yoF8lQP5KgfyVTzkqRzIVzmo6CsFyDrhcNhvCTkJ+SoH8lUO5KscyFfxkKdyIF/loKKvFCDrqFijTwXIVzmQr3IgX+VAvoqHPJUD+SoHFX2lAFlHxRp9KkC+yoF8lUPavlIRC0uovYqHPJUD+SoHFX3NT+fLjLEyAPcCqAEwAOBsznk7Y+wAADcAGAXwLOf8Cv3zvwJwgv769zjnb6ezf5GUlZX5LSEnIV/lQL7KIV1fKT62htqreMhTOZCvclDR13RHkL8G4D3O+SEA7gNwuf763wGcAeBgAPszxvZijO0F4DAA+wP4CoC/pblvoeTl5fktISchX+VAvsqBfJUD+Soe8lQO5KscVPQ1rQCZc/5nAFfpf84C0MYYqwRQxDlfxzmPAngGwBJowfKznPMo53wTgHzGWH06+xdJb2+v3xJyEvJVDuSrHMhXOZCv4iFP5UC+ykFFX12nWDDGLgBwqenl8zjn7zDGXgSwG4CjAFQCMDrRB2AugGEAnabXqwC0Gzc4OjqKpqYm1NXVoaenB+FwGI2NjWhtbUVZWRny8vLQ29uL+vp6dHV1IRqNor6+Hm1tbSgvLwcA9Pf3o6GhAe3t7QgEAqipqUF7ezsqKysRiUQwMDAwvs2CggJUVVUhHA5j+/btCIVCGBoaGn+/sLAQFRUV6OzsRHV1NYaGhjA8PDz+fnFxMUpKStDd3Y3a2lr09fUhFAqNv19SUoLCwkL09PRk/Jg6OjpQVVXl6zFVVFSgubk5p44pG85TOBxGW1tbTh1TNpynoqIidHR0pHxMg3kTjxFHRkay4piy4TxFo1E0NTXl1DH5fZ7C4TBaWlpy6piy4TwVFBSgs7Mzp44pG84TgLg+wK9j8kIgGo16+oIdjLGFAP4LYDGAZZzzXfTXLwFQACAEoJhzfq3++vsAjuKcdxi3s3Tp0ujChQuFaPJCc3MzZsyYkfH95jrkqxzIVzmk6+vm7cO44MFPAADPXrhYlCzlofYqHvJUDuSrHLLF1+XLl7+3ZMmSfdx8Nq0UC8bYzxhjZ+t/DgCIcM57AYQYY/MYYwEAxwB4DcAbAI5hjAUZY7MABM3BsZ+IulEg4iFf5UC+yoF8lQP5Kh7yVA7kqxxU9DWtKhYAbgNwp55+kQfgPP31bwD4l/7as5zztwCAMfYagKXQAvOL09y3UOrrsyYdOqcgX+VAvsohXV8DVMbCEmqv4iFP5UC+ykFFX9MKkDnnbQCOtXh9GYADLF7/NYBfp7NPWbS1tWH27Nl+y8g5yFc5kK9yIF/lQL6KhzyVA/kqBxV9pYVCdGIJ34RYyFc5kK9yIF/lQL6KhzyVA/kqBxV9pQCZIAiCIAiCIAxQgKzT39/vt4SchHyVA/kqB/JVDuSreMhTOZCvclDRVwqQdRoaGvyWkJOQr3IgX+VAvsqBfBUPeSoH8lUOKvpKAbJOe3t78g8RniFf5UC+yiFdX4vzqUu1gtqreMhTOZCvclDR13TLvOUMAarPJAXyVQ7kqxzS9bWurBAX7DsdNaXUtRqh9ioe8lQO5KscVPSVenGdmpoavyXkJOSrHMhXOYjw9bQ91HuUKBtqr+IhT+VAvspBRV/peaCOisP/KkC+yoF8lQP5KgfyVTzkqRzIVzmo6CsFyDqVlZV+S8hJyFc5kK9yIF/lQL6KhzyVA/kqBxV9pQBZJxKJ+C0hJyFf5UC+yoF8lQP5Kh7yVA7kqxxU9JUCZJ2BgQG/JeQk5KscyFc5kK9yIF/FQ57KgXyVg4q+UoCs09jY6LeEnIR8lQP5KgfyVQ7kq3jIUzmQr3JQ0VcKkHVaW1v9lpCTkK9yIF/lQL7KgXwVD3kqB/JVDir6SgGyTkFBgd8SchLyVQ7kqxzIVzmQr+IhT+VAvspBRV8pQNapqqryW0JOQr7KgXyVA/kqB/JVPOSpHMhXOajoKwXIOh0dHX5LyEnIVzmQr3IgX+VAvoqHPJUD+SoHFX0NRKNRvzXE8cILL7QDaPJbB0EQBEEQBJFTzF6yZEm9mw9mXYBMEARBEARBEH5CKRYEQRAEQRAEYYACZIIgCIIgCIIwkO+3AL9hjAUB3ARgDwAjAC7knK/1V5U6MMYKANwGYA6AIgBXAmgG8ASANfrHbuac388Y+xqAiwCMAriSc/5k5hWrA2PsfQA9+p8bAFwF4A4AUQAfA7iYcz5GvrqHMXYugHP1P4sB7AngIFB7TQnG2P4AruGcH84Ymw+X7ZMxVgLgHgBTAfQB+CrnvN2Xg8hCTL7uCeCvACLQrlHncM7bGGN/AfAZaP4BwIkAQiBfbTH5uhdc/u6pvTpj8vU+ALFVQeYAWMY5/4qK7ZVGkIGTABRzzg8E8FMA1/srRznOAtDJOT8EwHEAbgSwF4A/cs4P1/+7nzHWCOC70H4gxwC4mjFW5JvqLIcxVgwABg/PA/BHAJfrXgcAnEi+eoNzfkfMUwDvQfOO2msKMMZ+DOCf0G40AG/t85sAPtI/exeAyzOtP1ux8PUGAN/R2+zDAH6iv74XgGMM7bYH5KstFr56+d2TrzaYfeWcf0VvqycD2A7gUv2jyrVXCpCBgwE8DQCc82UA9vFXjnI8AOAXhr9HAewN4ATG2KuMsVsZYxUA9gPwBud8RP9hrAWwe+blKsMeAEoZY88yxl5kjB0AzddX9PefAvBZkK8pwRjbB8Aizvn/gdprqqwDcIrhby/tc7zfNXyW0DD7+hXO+Qf6v/MBDOtPPhcA+D/G2BuMsfP198lXe6zaq9vfPflqj9nXGFcA+CvnvEXV9koBMlCJicfYABBhjE361BO3cM77Oed9eufyILQ7wLcB/IhzfiiA9QB+hUSf+wCoVzk8cwwCuA7aKMY3APwLQIBzHis7E/OPfE2Ny6B14AC115TgnD8EIGx4yUv7NL5O3how+8o5bwEAxthBAL4N4E8AyqClXZwF4FgA32KM7Q7y1RaL9urld0++2mDhKxhjUwEsgZZyBSjaXilABnoBVBj+DnLOR/0SoyKMsZkAXgJwN+f8XgCPcM7f099+BMBiJPpcAe3xC2HNagD3cM6jnPPVADoBNBjej/lHvnqEMTYFwELO+Uv6S9RexTBm+Hey9ml8nbxNAmPsNAB/B3CCnqM5COAGzvkg57wPwIvQnjqRr+7x8rsnX73xJQD3cs4j+t9KtlcKkIE3ABwPAPpj7I/8laMWjLEGAM8C+Ann/Db95WcYY/vp/14CLdfzbQCHMMaKGWNVAHaGNpGHsOZ86PnwjLHp0O60n2WMHa6/fxyA10C+psKhAJ43/E3tVQzve2if4/2u4bOEBYyxs6CNHB/OOV+vv7wTgNcZY3n6ROmDASwH+eoFL7978tUbn4WWMhFDyfZKqQTaneNRjLE3oU0sOc9nPapxGYBqAL9gjMVykb8P4M+MsRCAVgBf55z36rNYX4N2Y/ZzzvmwL4rV4FYAdzDGXodWFeB8AB0AbmGMFQL4BMCDnPMI+eoZBu2RaoxvAriR2mva/AAu2ydj7GYAd+rtOwTgDN9UZzGMsTwAfwGwCcDDjDEAeIVz/ivG2L8ALIP2ePsuzvlKxtgGkK9ucf27p/bqmbg+lnP+iYrtlVbSIwiCIAiCIAgDlGJBEARBEARBEAYoQCYIgiAIgiAIAxQgEwRBEARBEIQBCpAJgiAIgiAIwgAFyARBEARBEARhgAJkgiAIgiAIgjBAATJBEARBEARBGKAAmSAIgiAIgiAMUIBMEARBEARBEAYoQCYIgiAIgiAIAxQgEwRBEARBEIQBCpAJgiAIgiAIwgAFyIQwGGOHMcaijLG/e/xegDH2NcZYsSxtomGMbWSMfdvlZwsYY98y/P1rxti78tQRBJGt6H1H1PDfKGOsiTF2HWOsLI3tljPGLjD8fQdj7EGX303aJzHGZjPGxhhjT6eo72TG2MxUvusHjLGXGWPXufxs3DWMMXYuY6xDrkJCNhQgEyI5C8AaAKczxko8fO9QAP8HIF+KKv85A8BvDH9fB+AYn7QQBOE/lwGYpv83G8D50PrPG9LY5g8AfNPw9yUALkxje2bOArAWwFFeA13G2GwADwOoEqgnmzBfw+4HsIt/cggR5GpAQmQYxlgRgC8B+B6Av+v/vtvl1wOSZGULccfHOe8H0O+TFoIg/KePc95q+HsLY+wGAD9B6kGtuZ/pSVWcDWdCCwK/C+BcAL/18N3J1scPARjySQshCAqQCVF8DkAlgP8BeBbaiMjdgPb4DsDnOOf7xD7MGHsZwLsAbgTwkv5yH2PsPM75HYyxo6F1wLsC6NA/dx3nPKp//0sAfgFgJwDrAVzGOX9Mf28fANcC2BdaIHoXgMs552HG2LnQOvhVAL6gf24+tN/CAv2/LwN4Rd//uQBKACwD8F3OOTcfOGMsH8AV0C4gOwDoAnAfgO8DOATA7frnogCOAHC40Y8keg8H8KC+rd8AqAbwKoALOedtjmeEIAiVGAUwEvuDMXYptBHhOdD6hf8C+CbnvF/vU/eH9hT4AAB/BPAr/XtRADsC+DWAcs75l5JtL5kwxtjeAHYG8CKAmQDOZYxdaeiPz4XWP9cZvnOHYf8b9Jc/YoxdwTn/tVO/p3//CAC/A7AHgBYAv+ec36K/twDak7hDAYxBG53+Pue8z9Bn3g7g69D64haTX9/lnN/JGPshgO8AqAXwAYAfcs6X2Xhg6R+AOpiuYfq/x/1IoneO7s+p0K45MwG8A+AbVtcbInNQigUhirMAvME5bwfwCIDDGGNzXXxvM4Av6v+eB+B+xtih0ALtJwAshvY48hcAvgUAjLEjoT3CuhvAbtBGNf7DGNuFMbYTtOB2FbSO92sAzobW0cZYDKAPwF7QOmVAC25vBnAkgDehBaMnADgNWsfKAbzCGLN6RPgjfR9fhRZg/wjAxQBO0rf1PWhB8zT973Fc6p0C4Bu6Tyfpei630EEQhGIwxoKMsf2gBWqP6q+dDi3A/T60PuVcACcCuMjw1WOh3SwfAOA2ANcD+BBaP7PZtA8323PiLACbOOfLofXvc6Hd6LtlP/3/hwO4Llm/xxhbCOBpAK8B2BNaf3cjY+woxlgNgNcBhKENQJwC4GBoHsSohTZ4she0wBSI9+t/jLGLoKWhfAvaNeF/AF5kjO1oFp/Ev4RrmOm7bvRC3/5F0AZRphl0Ez5BI8hE2jDGqgEcD+3xIAA8DiAC4Dxoga0tnPMIY6xL/3Mb53yIMfZdAE9zzq/UX1/NGJsBLVD+G7S79sc557EO5AbGWDmAUgBfgTai/B19dONT/c7/bn3UJcYVsUecjDFdCr9D/7sEwKUAlnDO39A//13G2DHQOvEbTYexEsC5nPNX9L83MsZ+BGAXzvlDjLEeAFHT/mJ83YXePGijDe/p378HWidPEISaXM8Y+73+7yIAUWj9ZqwP3QqtT3lS/7uJMfYK4vNahwBczTkfAwDGWD+AUZt+xs32LGGM5UHrV2OB32sA2qE9JXzJ7nsm2vX/d+oj4Mn6vQsArOSc/1j/3mr9OgNoczqCAM7WUxliI9jL9MA7xtWc83X6+0CiX5cB+Cnn/L/653+njz5fDOCHJv22/tlcw4zfTaY3pH/uSs75q/r7NwH4qYOfRAagAJkQwZcBFEJ7bATOeZeeQnEuY+xXKWxvEYB7TK+9DuD3jLEp0Dr1fxnf5JxfBQCMsd8CWBZ79Gf4bgG0O38AGDTl/wHAOsO/50G7aD2rP66MUQwgrufT9/04Y+wIxti10EYtdof2GC7P+TABaMeaTC8ArDb8u1d/nyAINbkaE31cCEAb53w8vYJz/gpjbB/G2JUAFkLrJxgmnngBwIZYsJcMl9uz47MAGjHRv0cYY48BOJMx9u0Uc52T9Xu7QEvBMx7DTQDAGDsFwAexYFPnHWg+7gJgu/6asU8HDH7pAyqzANzCGPuH4TNFMKS5GPadjn+Lkuj9QH+N+vgsgwJkQgRn6f9fb7hzDkKbuHA0tNERM05tz2pyQ2wSRBBax2K1TTfftfuM8bWYtqMAbDN9rtf8RX3E4xJoj8wegjYKdJ+NPqf9xjDrBSZGGcyfIQhCPTo452vt3tRHGG+Glkf7FLTc1CtMH3M9Cczl9uyI9e8vGvr3ALT+6XRok7L97uONmuw+Z/w7NnjxVQDvJ9t+mv651Ut9fJZBOchEWujlez4DLX9qT8N/e0PL8z0f2g+/yvCdALRJJDHMHeEnAA40vXYQtGC1G9qd9l4mHc8wxr6nf/cAfR/G74aROKJgx1poE2amcs7X6heydfox7mfx+YsB/IBz/gPO+d0ANkIr3RTTYNfRQ5BegiByi4sB/IFz/i3O+a0AVkAbWXUKmpz6mVS2B8ZYKbR5D7cgvn/fA9rEsvP1j4YAlOvpGDGMc1Cs+ninfs+qj7+FMXa9/t09TKVE94E24vqp0/HE0Ee9WwHsEOvj9X7+27AuwZnMv2R9fFp6CX+gEWQiXc4CMAzgBs75duMbjLE7oeXYPgRgPmPsm9AqXHwHQI3ho7FZ1Hszxt4D8AcA7zLGLoeW97YXgJ9Br2LBGPszgNeYtlDHU9Am0x0KbTJcD7QqFX9ljN0ILRC/HsCdnPMeU26YJXqO3E3QcptHoNV2vhTahcIqZaQTwAmMsVehVfL4NbRqE0WG4ytnjO0CLe/OyN/S1UsQRM7RCeAIvc8IQOt/dgHwkcN3+gE06pOjNwnYHgCcDKAcwPXmigqMsb9Bm3C3K7SUgSIAVzDGboWWdrcYWu5uTBsA7MkY24zk/d5NAC7RU+buhDaR72xoc12WQZvbEstXrgZwE4DnOeerGGNTkxxTjGsB/JIx1qLrPwNagHyYxWeT+We+hhn5VxK9c1zqJTIMjSAT6XImgPvNwbHOjdDukhsB/B7AlQDeg1bmxpiC8BGAJ6EFz1/nnH8AbabvqQA+hjaz+bf6/8E5XwrgHGid2Upooxgncc4/4ZxvhTZbeS9oM7r/CS1P7GKPx/VjTJQKWqFv77jYpA8T50Lr4D+CNgt9I4BboY2iA8AL0B7jvQ8tmB9HoF6CIHKHS6CNSr4L4HlowefVMI2qmngQ2lO7VdCC03S3B2j9+8s25cZuAzAI4Hx99PVSaH3xR9CCxz/HPsg574TWJ/4T2gRpx36Pc94E4PPQ+suV0AYdLuScv8g5H9S/WwktsH0YWv7yKUmOxcwN0CpFXAvNsy8D+JJhYraRZP7FXcOMXxSol8gwgWjU6ckAQRAEQRAEQUwuaASZIAiCIAiCIAyklYPMGNsfwDWc88NNr38ewC+hTXS6jXN+C2MsCC3vZg9oZVQudJrFSxAEQbiD+mKCIAixpDyCzBj7MbS8oWLT6wUA/gStvNdhAL7OGGuENsGpmHN+ILQC2Nenum+CIAhCg/pigiAI8aSTYrEO1knmOwNYyznv5pyHoCWjHwJtacWnAUBf63yfNPZNEARBaFBfTBAEIZiUUyz0JXTnWLxVCa3UVow+aDVwza9HGGP5nPNR45dfeumlaCAQQH5+PiKRCKLRKAoKChAOh5GXp5VYjEQiKCgoQMdACNuHExcSmlIURF15IUZHtU3n5+dbfj8cDiMQCCAvLw+jo6PIy8tDNBrF2NjY+PvBYBDBYBCjo6PIz8/H2NiY5/cDgQAikQjy8/PHNdkdUzLNmTymcDiMgoKCpMeU7DzJPqZQKIS8vDyh50nGMUUikbhzn+m25/aYAoEAxsbGfG17bo4pGAwiGo362vbcaI5tf2RkpGPJkiX1CR1WmlBfnNt9cayN+9VvuT2mYDA4/v9s7osDgQCCwaCvbc/NMY2NjaGwsND3OCDX+uJIJOK6H5ZRB7kXQIXh7wpoSz+aXw+aO2QAKC4uxsKFC13taNmmHvzzxY0YHp3omIvzg7jsyDnYZVaVwzf9Y2BgAGVlZX7LcIUqWkmnWEinWGI6ly9f3pThXVNf7IBq7SfbIZ1iUUUnoI7WgYGBWAlBV8ioYvEJgAWMsRrGWCG0BRyWAngDWpFvMMYOQPIC5UnZd0YlFk4txZh+51KcH8TCqaXYd0ZlupuWRk9PKsvW+4MqWkmnWEinWHzUSX2xA9R+xEI6xaKKTkAdrV51CguQGWNnMMa+zjkPA/g+gGegdca3cc63AHgEwDBj7E1oE0cuTXefecEArj52Pvr6QhgcGsVlR87B1cfOR14we5cwjw3zq4AqWkmnWEinWDKtk/pid1D7EQvpFIsqOgF1tHrVmXULhSxdujTq9rFejMXXvAkAeP8nB8mQJJSRkREUFRUl/2AWoIpW0ikW0imWmM7ly5e/t2TJEmUmxFFfnB2QTrGQTvGoonVkZAQrV6503Q/TQiEZprW11W8JrlFFK+kUC+kUiyo6JxuqnBfSKRbSKR5VtHrVSQFyhlEhkT2GKlpJp1hIp1hU0TnZUOW8kE6xkE7xqKLVq04KkDNMrHSJCqiilXSKhXSKRRWdkw1VzgvpFAvpFI8qWr3qpAA5w/T29votwTWqaCWdYiGdYlFF52RDlfNCOsVCOsWjilavOilAzjD19cLXCZCGKlpJp1hIp1hU0TnZUOW8kE6xkE7xqKLVq04KkDNMV1eX3xJco4pW0ikW0ikWVXRONlQ5L6RTLKRTPKpo9aqTAuQMk21l9ZxQRSvpFAvpFIsqOicbqpwX0ikW0ikeVbR61UkBcoZR5VEEoI5W0ikW0ikWVXRONlQ5L6RTLKRTPKpopRSLLKetrc1vCa5RRSvpFAvpFIsqOicbqpwX0ikW0ikeVbR61UkBcoYpLy/3W4JrVNFKOsVCOsWiis7JhirnhXSKhXSKRxWtXnVSgEwQBEEQBEEQBihAzjD9/f1+S3CNKlpJp1hIp1hU0TnZUOW8kE6xkE7xqKLVq04KkDNMQ0OD3xJco4pW0ikW0ikWVXRONlQ5L6RTLKRTPKpo9aqTAuQM097e7rcE16iilXSKhXSKRRWdkw1VzgvpFAvpFI8qWr3qpAA5wwQCAb8luEYVraRTLKRTLKronGyocl5Ip1hIp3hU0epVJwXIGaampsZvCa5RRSvpFAvpFIsqOicbqpwX0ikW0ikeVbR61UkBcoZR5VEEoI5W0ikW0ikWVXRONlQ5L6RTLKRTPKpopRSLLKeystJvCa5RRSvpFAvpFIsqOicbqpwX0ikW0ikeVbR61UkBcoaJRCJ+S3CNKlpJp1hIp1hU0TnZUOW8kE6xkE7xqKLVq04KkDPMwMCA3xJco4pW0ikW0ikWVXRONlQ5L6RTLKRTPKpo9aqTAuQM09jY6LcE16iilXSKhXSKRRWdkw1VzgvpFAvpFI8qWr3qpAA5w7S2tvotwTWqaCWdYiGdYlFF52RDlfNCOsVCOsWjilavOilAzjAFBQV+S3CNKlpJp1hIp1hU0TnZUOW8kE6xkE7xqKLVq878VHbCGAsCuAnAHgBGAFzIOV+rv9cI4D7Dx/cE8FPO+d8ZY+8D6NFf38A5Py+V/atMVVWV3xJco4pW0ikW0ikWmTqpL04daj9iIZ1iUUUnoI7WqqoqT6PIKQXIAE4CUMw5P5AxdgCA6wGcCACc81YAhwMAY+xAAFcBuIUxVqy/f3iK+8wJOjo6UFZW5rcMV6iilXSKhXSKRbLOk0B9cUpQ+xEL6RSLKjoBdbR2dHR4+nyqAfLBAJ4GAM75MsbYPuYPMMYCAP4K4EzOeUT/TClj7Fl9v5dxzpeZvzc6OoqmpibU1dWhp6cH4XAYjY2NaG1tRVlZGfLy8tDb24v6+np0dXUhGo2Of7ezsxMA0N/fj4aGBrS3tyMQCKCmpgbt7e2orKxEJBLBwMDA+DYLCgpQVVWFjo4OVFVVIRQKYWhoaPz9wsJCVFRUoLOzE9XV1RgaGsLw8PD4+8XFxSgpKUF3dzdqa2vR19eHUCg0/n5JSQkKCwvR09ODurq68eNLdkz19fVoa2tDeXm5b8c0PDyM/v7+pMfk9jzJOqbh4WG0tLQIPU8yjglA3LnPdNtze0wlJSVoamryte25OaaioiJs3brV17bn5pjGxsbQ29ubYlebFOqLc7wvLigoQFNTk2/9lttjKi0tRVNTk2/9lttjCgaD6Ojo8LXtuTmmkZERDA8P+x4H5Fpf7IWAsVNzC2PsnwAe4pw/pf+9CcBczvmo4TNfAPBFzvlX9b93A3AAgH8CWADgKQDM+B0AWLp0aXThwoWe9Cy+5k0AwPs/OcjzsWSabdu2YerUqX7LcIUqWkmnWEinWGI6ly9f/t6SJUsSAth0oL44dVRrP9kO6RSLKjoBdbRu27YNzc3NrvvhVEeQewFUGP4OmjtXAGcBuMHw92oAaznnUQCrGWOdAKYB2JyiBiUZGhryW4JrVNFKOsVCOsUiWSf1xSlC7UcspFMsqugE1NHqVWeqVSzeAHA8AOh5bx9ZfGZvAG8a/j4fWn4cGGPTAVQCaElx/8qiSr1AQB2tpFMspFMsknVSX5wi1H7EQjrFoopOQB2tmaqD/AiAYcbYmwD+BOBSxtgZjLGvAwBjrB5Anz5CEeNWAFMYY68DuB/A+RYjHTmPKvUCAXW0kk6xkE6xSNZJfXGKUPsRC+kUiyo6AXW0etWZUooF53wMwDdML39qeL8dWkkh43dCAM5IZX+5RGFhod8SXKOKVtIpFtIpFpk6qS9OHWo/YiGdYlFFJ6COVq86aaGQDFNRUZH8Q1mCKlpJp1hIp1hU0TnZUOW8kE6xkE7xqKLVq04KkDNMrPyRCqiilXSKhXSKRRWdkw1VzgvpFAvpFI8qWr3qpAA5w1RXV/stwTWqaCWdYiGdYlFF52RDlfNCOsVCOsWjilavOilAzjCqlEMB1NFKOsVCOsWiis7JhirnhXSKhXSKRxWtmSrzRqTI8PCw3xJco4pW0ikW0ikWVXRONlQ5L6RTLKRTPKpo9aqTAuQMo0q9QEAdraRTLKRTLKronGyocl5Ip1hIp3hU0ZqpOshEiqhSLxBQRyvpFAvpFIsqOicbqpwX0ikW0ikeVbR61UkBcoYpLi72W4JrVNFKOsVCOsWiis7JhirnhXSKhXSKRxWtXnVSgJxhSkpK/JbgGlW0kk6xkE6xqKJzsqHKeSGdYiGd4lFFq1edFCBnmO7ubr8luEYVraRTLKRTLKronGyocl5Ip1hIp3hU0epVJwXIGaa2ttZvCa5RRSvpFAvpFIsqOicbqpwX0ikW0ikeVbR61UkBcobp6+vzW4JrVNFKOsVCOsWiis7JhirnhXSKhXSKRxWtXnVSgJxhQqGQ3xJco4pW0ikW0ikWVXRONlQ5L6RTLKRTPKpo9aqTAuQMo0q9QEAdraRTLKRTLKronGyocl5Ip1hIp3hU0Up1kLMcVeoFAupoJZ1iIZ1iUUXnZEOV80I6xUI6xaOKVqqDnOWoUg4FUEcr6RQL6RSLKjonG6qcF9IpFtIpHlW0Upm3LKewsNBvCa5RRSvpFAvpFIsqOicbqpwX0ikW0ikeVbR61UkBcobp6enxW4JrVNFKOsVCOsWiis7JhirnhXSKhXSKRxWtXnVSgJxh6urq/JbgGlW0kk6xkE6xqKJzsqHKeSGdYiGd4lFFq1edFCBnGFXutAB1tJJOsZBOsaiic7KhynkhnWIhneJRRSuNIGc54XDYbwmuUUUr6RQL6RSLKjonG6qcF9IpFtIpHlW0etVJAXKGUaVeIKCOVtIpFtIpFlV0TjZUOS+kUyykUzyqaPWqMz+VnTDGggBuArAHgBEAF3LO1xre/z6ACwC06y9dBGCN03cmC62trZg9e7bfMlyhilbSKRbSKRaZOqkvTh1qP2IhnWJRRSegjtZM1UE+CUAx5/xAAD8FcL3p/b0AnMM5P1z/j7v4zqSgrKzMbwmuUUUr6RQL6RSLZJ0ngfrilKD2IxbSKRZVdALqaPWqM9UA+WAATwMA53wZgH1M7+8N4GeMsdcZYz9z+Z1JQV5ent8SXKOKVtIpFtIpFsk6qS9OEWo/YiGdYlFFJ6COVq86U0qxAFAJwDgdMMIYy+ecj+p/3wfgbwB6ATzCGPuci+8AAEZHR9HU1IS6ujr09PQgHA6jsbERra2tKCsrQ15eHnp7e1FfX4+uri5Eo9Hx73Z2dgIA+vv70dDQgPb2dgQCAdTU1KC9vR2VlZWIRCIYGBgY32ZBQQGqqqrQ0dGBqqoqhEIhDA0Njb9fWFiIiooKdHZ2orq6GkNDQxgeHh5/v7i4GCUlJeju7kZtbS36+voQCoXG3y8pKUFhYSF6enpQV1eHLVu2oLe3N+kx1dfXo62tDeXl5b4d0+bNm1FQUJD0mNyeJ1nHtHnzZgwPDws9TzKOqbW1Ne7cZ7rtuT2mUCiE3t5eX9uem2OKbdvPtufmmAYGBmReQKgvzvG+uKenB729vb71W26PKRKJoLe317d+y+0xxbT62fbcHFN3dzdKSkp8jwNyrS/2QsDYqbmFMfZHAMs45//R/27mnM/Q/x0AUMk579H//haAWgDVdt8xsnTp0ujChQs96Vl8zZsAgPd/cpDnY8k0g4ODKC0t9VuGK1TRSjrFQjrFEtO5fPny95YsWSJ0tJb64tRRrf1kO6RTLKroBNTROjg4iE8//dR1P5xqisUbAI4HAMbYAQA+MrxXCeBjxli53kEfCeC9JN+ZNHR1dfktwTWqaCWdYiGdYpGsk/riFKH2IxbSKRZVdALqaPWqM9UUi0cAHMUYexNAAMB5jLEzAJRzzv+PMXYZgJegzZB+gXP+P322ddx3Uty30qQyYu8XqmglnWIhnWKRrJP64hSh9iMW0ikWVXQC6mj1qjOlAJlzPgbgG6aXPzW8fzeAu118Z9JRX1/vtwTXqKKVdIqFdIpFpk7qi1OH2o9YSKdYVNEJqKO1vr4e7e3tyT+oQwuFZJi2tja/JbhGFa2kUyykUyyq6JxsqHJeSKdYSKd4VNHqVScFyBkmNnNTBVTRSjrFQjrFoorOyYYq54V0ioV0ikcVrV51UoBMEARBEARBEAYoQM4w/f39fktwjSpaSadYSKdYVNE52VDlvJBOsZBO8aii1atOCpAzTENDg98SXKOKVtIpFtIpFlV0TjZUOS+kUyykUzyqaPWqkwLkDONlBqXfqKKVdIqFdIpFFZ2TDVXOC+kUC+kUjypaveqkADnDBAIBvyW4RhWtpFMspFMsquicbKhyXkinWEineFTR6lUnBcgZpqamxm8JrlFFK+kUC+kUiyo6JxuqnBfSKRbSKR5VtHrVSQFyhlHlUQSgjlbSKRbSKRZVdE42VDkvpFMspFM8qmilFIssp7Ky0m8JrlFFK+kUC+kUiyo6JxuqnBfSKRbSKR5VtHrVSQFyholEIn5LcI0qWkmnWEinWFTROdlQ5byQTrGQTvGootWrTgqQM8zAwIDfElyjilbSKRbSKRZVdE42VDkvpFMspFM8qmj1qpMC5AzT2NjotwTXqKKVdIqFdIpFFZ2TDVXOC+kUC+kUjypaveqkADnDtLa2+i3BNapoJZ1iIZ1iUUXnZEOV80I6xUI6xaOKVq86KUDOMAUFBX5LcI0qWkmnWEinWFTROdlQ5byQTrGQTvGootWrTgqQM0xVVZXfElyjilbSKRbSKRZVdE42VDkvpFMspFM8qmj1qpMC5AzT0dHhtwTXqKKVdIqFdIpFFZ2TDVXOC+kUC+kUjypaveqkADnDqHKnBaijlXSKhXSKRRWdkw1VzgvpFAvpFI8qWmkEOcsJhUJ+S3CNKlpJp1hIp1hU0TnZUOW8kE6xkE7xqKLVq04KkDPM0NCQ3xJco4pW0ikW0ikWVXRONlQ5L6RTLKRTPKpo9aqTAuQMo0q9QEAdraRTLKRTLKronGyocl5Ip1hIp3hU0Up1kLMcVeoFAupoJZ1iIZ1iUUXnZEOV80I6xUI6xaOKVq8681PZCWMsCOAmAHsAGAFwIed8reH90wF8D0AEwAoA3+KcjzHG3gfQo39sA+f8vFT2rzKFhYV+S3CNKlpJp1hIp1hk6qS+OHWo/YiFdIpFFZ2AOlq96kwpQAZwEoBizvmBjLEDAFwP4EQAYIyVALgSwG6c80HG2L8BfI4x9iwAcM4PT3GfOUFFRYXfElyjilbSKRbSKRbJOk8C9cUpQe1HLKRTLKroBNTRWlFRgZaWFtefTzXF4mAATwMA53wZgH0M740AOIhzPqj/nQ9gGNoIRylj7FnG2It6Zz7p6Ozs9FuCa1TRSjrFQjrFIlkn9cUpQu1HLKRTLKroBNTR6lVnqiPIlZh4PAcAEcZYPud8lHM+BqANABhj3wFQDuA5ALsCuA7APwEsAPAUY4xxzkeNGx4dHUVTUxPq6urQ09ODcDiMxsZGtLa2oqysDHl5eejt7UV9fT26uroQjUbHvxs7+P7+fjQ0NKC9vR2BQAA1NTVob29HZWUlIpEIBgYGxrdZUFCAqqoqdHR0oKqqCqFQCENDQ+PvFxYWoqKiAp2dnaiursbQ0BCGh4fH3y8uLkZJSQm6u7tRW1uLvr4+hEKh8fdLSkpQWFiInp4e1NXVIRKJoKmpKekx1dfXo62tDeXl5b4d08jICPr7+5Mek9vzJOuYRkZG0NLSIvQ8yTimYDAYd+4z3fbcHlNZWRmampp8bXtujqm4uBhbt271te25OaZoNIre3t4Uu9qkUF+c431xYWEhmpqafOu33B5TeXk5mpqafOu33B5Tfn4+Ojo6fG17bo4pHA5jeHjY9zgg1/piLwS8fgEAGGN/BLCMc/4f/e9mzvkMw/tBANcC2AnAV/THe0UAgpzzIf0zbwP4Iud8s3HbS5cujS5cuNCTnsXXvAkAeP8nB3k+lkzT1taGhoYGv2W4QhWtpFMspFMsMZ3Lly9/b8mSJfsk/4Z7qC9OHdXaT7ZDOsWiik5AHa1tbW3YsmWL63441RSLNwAcDwD647mPTO//A0AxgJMMj/fOh5YfB8bYdGgjH+6TQXKE4eFhvyW4RhWtpFMspFMsknVSX5wi1H7EQjrFoopOQB2tXnWmmmLxCICjGGNvAggAOI8xdga0R3jvArgAwGsAXmSMAcANAG4FcAdj7HUAUQDnmx/pTQZUqRcIqKOVdIqFdIpFsk7qi1OE2o9YSKdYVNEJqKO1sbHRUx5ySgGyntv2DdPLnxr+bTcyfUYq+8slWltbMXv2bL9luEIVraRTLKRTLDJ1Ul+cOtR+xEI6xaKKTkAdrV7rINNCIRmmuLjYbwmuUUUr6RQL6RSLKjonG6qcF9IpFtIpHlW0etVJAXKGKSkp8VuCa1TRSjrFQjrFoorOyYYq54V0ioV0ikcVrV51UoCcYbq7u/2W4BpVtJJOsZBOsaiic7KhynkhnWIhneJRRatXnRQgZ5ja2lq/JbhGFa2kUyykUyyq6JxsqHJeSKdYSKd4VNHqVScFyBmmr6/PbwmuUUUr6RQL6RSLKjonG6qcF9IpFtIpHlW0etVJAXKGCYVCfktwjSpaSadYSKdYVNE52VDlvJBOsZBO8aii1atOCpAzjCr1AgF1tJJOsZBOsaiic7KhynkhnWIhneJRRatXnRQgZxivdfj8RKbWyFgUyzb14J73W7FsUw8iY96XPI+hiqekUyykk0gHVc4L6RQL6RSPKlq96kx1JT0iRVQphwLI0xoZi+JnT6/Fp9sGMTI6hqL8IBZOLcXVx85HXjDgeXuqeEo6xUI6iXRQ5byQTrGQTvGoopXKvGU5hYWFfktwjSyt7zT34tNtgxgeHUMUwPDoGD7dNoh3mntT2p4qnpJOsZBOIh1UOS+kUyykUzyqaPWqkwLkDNPT0+O3BNfI0rq2cwgjo2Nxr42MjmFd51BK21PFU9IpFtJJpIMq50WmTpGpbuSnWFTRCaij1atOSrHIMHV1dX5LcI2d1shYFO8092Jt5xDm15Zg3xmVnlIj5teWoCg/iGFDkFyUH8S82tQe06jiKekUC+kk0kGV8yJLp+hUt8nup2hU0Qmoo7Wurg6dnZ2uP08jyBlGlTstwFprrFP93Ysbcfd7Lfjdixvxs6fXehp52HdGJRZOLcVYNIpoNIpivWPed0alMJ3ZCOkUC+kk0kGV8+KkM50RYNGpbrngZzahik5AHa00gpzlhMNhvyW4xkqrsVMF4jvVA2ZVudpuXjCAq4+dj4P+/Bby84P47THzPI9CJ9OZTcRG3N9b14u9R8vSOtZMkO1+xiCdRDqocl7sdKY7AuyU6ua2L3ejM9sgneJRRatXnRQgZxi/6wV6SY+w0iqqU80LBhAKjyEUHkupM06mM1swX8SeadqY1mPMTJDNfhohnUQ6qHJe7HSmO1ghOtVNdT+zDVV0AupobWxspBQLP3D7qMvPeoFe0yOstMY6VSPpdKoiyOYajKIfY2aCbPbTCOkkrFChLwbS15nuZGfRqW5+++kW0ikeVbRSHWQf8PKoq6yszCeV3kccrLTGOtXlW/oQAFBSkJdWpyoCPz1NhujHmJkgm/00QjoJM6r0xSJ0pjsCLDrVTZV2TjrFo4rWsrIyGkHONF5GCfPy8jIvUMfriIOV1lin2tcXwuDQKC47co7v6QJ2noosYZQq2TLi7sULP9uoF0gnYUaVvliEThEjwLFUt8GhURwwqyqtfjzb23msD3x0dZ/U64Go6062+2lEFa1eddIIsgC8jBL29vaiuro6pf1kuryanVaR+cMisNIpuoRRqmTDiLtXL9Jpo5mEdBJmVOmLRegUPQKcLk5+putXupj7wMdX90q5Hoi87qjUb6iitbfXW2ojBcjIbOBZX1+fssZ0f3heg7VUtWYaK50iqm2IQNRFLJ026tULlc97NqKKThVI9jtQpS8WpTObBivsdGbDYEWmrgci96NCvxH7PX7SGsDOgZ6sr9BUX1/vKcVi0gfIbn+8Th2zl8Czq6sLpaWlnnX+f3tnHiTJWSX2X181XdOXpqd7uueQZnSMvlkMCB2wYhFCMBALkrWwDuMgWGOuZQF7w6zkWAgwmMVeAyuDWINjWUMIAys2jLHR7sqxXBGSQRKj1aKRiAHEpwNNzwhN9/Sl6mO6p/oo/9FVrZruOjKrX1bmy3m/iI7oqrx+L78vX77KzPoqjuHVGnVtNpU8k/Ts71ZPYkH6aK3+GXZfxN3uQT8MxO0ZFC2eSaBW2wc5DrTk4mZ4Nptqnkm4WNGs80GQ7dTLb6XpR381ylWXDCe26Nx0PD42nfgRmqampkLNf94XyEEO3nqJOUzhWSg09jxSHMOrNeraLJ5PJPNctXrup1fpIYzipF4frdc/w+6LONs9zNWmpPfPElo846Ze2wfJ1VpycTM8m001zyRcrGjW+aDedur18Y3Tv3s8ucOCSnzwafajN2GPpYYKZOdcK/AXwBXAWeD3vfdPlk2/GfgPwDLwFe/9l+stExdBDt6giTlI4dnobZM4Cr4k3+Kpl0iS8OyvFPX6aL3+KfVoTTOSWZiku5X+2czEHOVxlKZcXK/tgxZaWnJx1J7NpppnEi5WNOt8UG879fp4Eq62l6iXI7f6wSeOR28GBwcZHx8PPH+jV5DfBHR671/unLsW+CzwRgDnXAfwOeClwDzwgHPubuC3qi0TFWeW1r4hXIt9fZ0VD969fZ3ML62991ixAcs5u7zKY6fneeFw96Z1zuVXqm7v1Ngku3eHH1T7N3Z1cXAgy09Pza0feAcHshza1cVsje1tomyHzJ6tvdyp0UmG67jOlK2j0escucXNHoUKayt/5+EKieSx02e491fTXF1MRh+64QA3/uXDtLe38qFXH+DKPT3MhNlXG7cvcCFnamE59DJD3ZlNfTTT3squ7gxTC8scG52r2D9/NjrP5YNrw+988FUHuOm/r+2LD96wti9yG9p/dbXAI8/O8rOT47zwwkGu3NNDazFRra4W+OQ9T68nxW3trVy2M8tHXnPx+jyhqbA/j52qEctA1/Oep8o8d/eEclhdLfDJeyvE8ur6sbS0trCjM9w3ocfGxti/f3+oZULwJhKeixeWVjmztFp3vmptf2x0noMDXQx1Z8i0t54zT+k4mDxT+biq9j7A6NgEw0Phc/Gl/du5dGeWY6Nrubizo41Ld2a5tH97ze3VQsKz0W1vdfnV1QKPlh2PL9lwPEaxvxrhj68/wD/90vM58CW7e3iuwrknyu3U6+P1pjeL8hyZX14lUyFHNnI8lnP01zPn1Falc/g9T01z1d76H1y2d7SS7Qg3ENvY2Fio+VsauX3jnLsdeMh7/z+Lr3/tvd9b/P/FwG3e+9cXX38O+DHw8mrLlHPHXT8o/Kd/yId22gp9PRnaO1ppYe2cvby0Sm72eYdMRys9PRlaW54/6FcLBWZn8+QDJP4wZDpaaW9vZXl5teK6600/X9iebWd7tp2WsjYpFAqcWVjmTANFaNKp1Uel+qfUNrbSR4Nsp97xKrGNKLjjdZ0PHz58+BrJdaYpFzej7aV9LRcHbxPbX/X7eLPy7FY9S2zleIzrHB4mDzd6BbkXyJW9XnHOtXvvlytMmwX66iwTK7nZfM3Oll9aZXlpdVNHkO6UQTpbPoLtamS5OH5o+fW+QvH9NFKrj0r0z0xHK+0dresJsQVo72gl09FKfmmV9vZWNl5bbQHa21vFEmaQWOp5nhNPleM5aCxKTuipycVB+nG9XN1s3wT3i6YQ9HiE5uwvib4R5UWqen08aC4PkmeDeFabJ2iO3MrxqOEc3miBPAP0lL1uLUuuG6f1AM/VWWadFwx388iHDoUWGhkZ2XQb8xdjcyytrF0hf8dfHQPgq297Ueh1w9qnp1+ePsOzs2fZ07ONQ7u2n/PpKgi1HH4xNs83Hh0lX/RtAbqz7bz35ft4wVC4WytbjVViHVE7rBYKfPmhZznx3CL5lQKZthYuuiDLe268LFS71PNcLRR43zd/QXt7K3/4yos2tXu96eWMjY0xNDTUkEc9tto/f/DEFN9/4txv+La2tPAvrtnNay/r39Q/Ye1RpHe98qL1/hmkDwfZX7ViqedZ6hePT5yhpeh40QWdvOdle9bXUS+WWuvY1t7OC4bCjShQyk1Hjx4NtVxAEp+LJ88s88xzaz9GFOR422qeDUK93BL0mG50G81aR9Sx1DseJbYRZJ4gx32Janm43jrCbKMW9fp4afrjpya5fPfOTdPr5dkgnvXmCZLvg+7Tav0vzDm8UrvvuyDLzu3hStiRkZGmDPP2AHAz8L+Kz7AdK5v2GHDQOdcPzAHXA59h7cNBtWW2TKXGGejetukxx6HezprrqfXAyXCfzJcKdlVwOHJiZr2YL7G0UiB3doVdPbWdq26n6nLBH6sZ7NkWapsbn9gZ6A63fKX17Ox6fh3lzyZ/6NUHOHZqjuNTZzjQv50X7e5u+GTa35XZ9N5qocDtPxqhpydDC/DXj45yyc4st16/fz1h1pq+ke59w2QyHTU9dmzf7BGUV1zc2L4GcINd/PBX05wtT4htLRwc6OKC7RmuPdDBkRM5np5aWE9mF/dnufbABeuxTi3kKvbhqcVlLtieYbVQ4HMb9tfF/VluqbC/fqtKLJfX8fzps7OcfG5xfX35lQInn1vkZC7PFXvWasJ6sdRax9X7wh+L1T4UCZH4XLy9o5V9F5ybOze+LueiHc0b0myjR+nZy/U++tOxwM+nB91Gs9bRjFiu2NPDD59+7pznULe1t/Li3T2hnFdXC+tXIk/PL216jrnePEd/PcPJ3Nlzj9ncWSbOLG96nnXX9t1kMpvzS711hNlGPer18Yt2bOeGi3sqej50snKtMLe0yr4LsoE8682zp7eTh4pf0Ct/Bvm1B3dW7DuV9mmQ/veJ113Ko6dmOT69yIEdnRXbvZzyPrU95PPHsJabmlEg3wW8zjn3Y9Y+wLzTOfdWoNt7/yXn3K3A91j7KeuveO9/7ZzbtEyD267I+Pg4F1544Tnv7eraXIwMd9cuUJrB7goOL9nTzd//cmLTlwVfsqeb3T2NOTe6XDl7ehov2AD29m5teYB9fdXXsf+CTk6ePMmFFw6EXu9KWcL9dW5x07d0HzyR4+mp5wulsysFnp5a5NTMWa69qK/u9I2cPHmaCwcv3PR+ORdd0HiRuxX29ma47/j0pm8U33Ro5/o++dzNl/OPz8zw1OQCl1b4VvPV+3r43uOTm/rw1Xt72H/BNh48keP49Ln76/j0ImOzlfdXJfb1Zri/hud9T0+fc9UD1pL/7Nll9pft21qx1FpHI/25Um4SJPG5OFvhyzRhr/xIUn7cPzExf07bP3gix1OTC8/30eK38p+aOtPwKAISsTayjmbE8ppLd/CDJyY3HY+vuXRH4JEISqMZlAqpLzxwsuI477XmGZvLk99waz6/vMrpufwm55OTp9hd4Xist44w25CgmueLdndz92Oba4UXDXcF9gwyz2duOlgz39dzDdr/Xnew9p2GWsdrWMKMYAENFsje+1XgfRve/mXZ9LuBuwMsI0ZLBLfhJClv5AdPbP7FmdLwMBsTjcZhyerFKk0jbb8x4X7yns3jTdYbxibsMDdJ7qOlcVn/8ZkZHnl6lCsv3jxAfVtrC9de1Ff15FqvD0uOH1vNM+iQUrVikR6WKsp2t1wcjnrHfRLG7C133UoebUYsQfJGPUpDm5UKqUpDm9WbJ8wxW61/1ltHs4erq+ZZL88G8QwyT718X89Vov8FOU+HIWxuSs0PhfT3h3veqZkEaeTyRBPkE1vUvo0mZukOHYRG2j5IUpZOmEnuo/B8QnzxQEdDv9xVrw9Ljh9bzVPig6b0h9Wkt7s0SY5XstCKEok8KhVLvfPBVvNGkEKq3jxhjtlq/bPeOpp9EauaZ708G8SzGTlOov8FOU+H9Tx9+nTg+VNTII+Pj0c51uiWCNrIYT6xRcVWE7N0hw5CI20fJClLJ8xqns2+4l6PrRxLtfqwdFKu5CnxQVP6w2qSc1MUJDleyUIrSiTyqEQsYc4Hjba7xBXPMMdsNc9662j2Raxa+7NWng3i2YwcJ9H/pO+CNOURiyTS25vcRxGSdNuuHltNzHHE2kjbB73FJJkwK3nGccW9HlEdS9JJuZqnxAdNyQ+rSc5NUZDkeCULrSiRfhyp0VjCnA8abXepK55Bj9lanvXW0cyLWFs5joJ4Rp3jJPqf9B2d3t7epnxJL3GsrMj/Io4USbltF4StJuZm3dY7Z94G2j7op1vJhFnJM44r7vWI8liSTMpJPubL0eIpRZLjlSy0okT6caRGYwlzPmi03Zt9xTPJ/bMcLZ5Q3XWr/U/6jk7YfZqaAnl+fp6BgfAjGTSDZt622+rt+q0m5mbf1oPG2j6OK0WVPJN4dyHJx1I55plMkhxv+XH/8FOnuPrS3ZEc91vNw0l51CPM+WAr7d7MK55J7p/laPGE6Fylz9Pz8/Oh5k9NgTw8XP236uN+xrO8kR8fm+XyoZ7IkvJWb9dvNTE3+7Ye1G77eq7NvFJUyTOJdxca3Z/NxjyTSbV4487DJUrH/ZVDnWzbJj+sokQebtY5ox5hzgda+rl5yhOlq+R5enh4ONQjFuFHWk4oo6OjFd8vT1bbs+188p7jfPi7T7KyGvzHMiQoNfKrdq1w7UV9kSS68sKypaXlnMIyjOenXn8ZH3nNAX7n0iwfec2B0M/DlmL9vSuHG4q11lXVSlRr+6RRybN0Auos/rRnZwKG99O8P5OIFk8pKsWblDxcTlTtIpGHoTnnjCAOpfPBv7p6d83zgZZ+bp7yaHEN65maK8gdHZV/FCNpz3hW85RA6nZ9KTFf1D7Pnj3N30dhr6pGuU8lqeSZlC8FlaN5fyYRLZ5SVIo3aXkYomsX6cem4u4/Qa/gxe0ZFPOUR4trWM/UXEHu66t88Ia9Ghk11TwlKBWW5Wzldn2UrrUIe1U1Ls+wVPPc6hV3abTvz6ShxVOKSvEmLQ9DdO2SljwcFvOURYsn6HEN65maAnliYqLi+9LJaqtU85RA+nZ9lK61CHNbD+LzDIt5ymKeyaRSvEnLwxBdu6QlD4fFPGXR4gl6XMN6puYRi2qfDJLybeASUX7Skr5dH+enwjAP5qf102tcmKcsWjylqBRv0vIwRNcuacrDYTBPWbR4gh7Xvr6+83Mc5Hw+X/H9pD3jWc1TCslvfEbtKoV5ymKesmjxlKJSvEnLwxBtu1geTi7mKY8W17CeqSmQFxaqP8uWhIHfS9TyTBpaXM1TFvOURYunFNXiTVIeBj3tYp6ymKc8WlzDeqbmGWQtYwZq8QQ9ruYpi3nKosVTCi3xmqcs5imLFk/Q4xrWMzUFclrH4YsTLa7mKYt5yqLFUwot8ZqnLOYpixZP0OMa1jM1BXImk4lbIRBaPEGPq3nKYp6yaPGUQku85imLecqixRP0uIb1TE2B3NPTE7dCILR4gh5X85TFPGXR4imFlnjNUxbzlEWLJ+hxDeuZmgI5zNAdcaLFE/S4mqcs5imLFk8ptMRrnrKYpyxaPEGPa1jP1BTIO3bsiFshEFo8QY+recpinrJo8ZRCS7zmKYt5yqLFE/S4hvVMTYGc1mFG4kSLq3nKYp6yaPGUQku85imLecqixRP0uJ63w7wtLi7GrRAILZ6gx9U8ZTFPWbR4SqElXvOUxTxl0eIJelzDeqamQE7rOHxxosXVPGUxT1m0eEqhJV7zlMU8ZdHiCXpcbRzkhKPFE/S4mqcs5imLFk8ptMRrnrKYpyxaPEGPa1jPhn5q2jmXBe4EdgGzwNu99+Mb5rkFeEvx5d977z/hnGsBngGeKL5/xHv/4UYcNtLZ2SmxmsjR4gl6XM1TFvOUJSrPJOZhsHaRxjxlMU95tLiG9WyoQAbeDxzz3v+Jc+4twEeBD5QmOucuAX4P+E2gANznnLsLOAMc9d7f3OB2q5LNZqVXGQlaPEGPq3nKYp6yROiZuDwM1i7SmKcs5imPFtewno0WyNcBtxX//w7wsQ3TTwKv996vADjnOoBF4Gpgr3PuXmABuMV778sXXF5eZmRkhIGBAXK5HEtLSwwPDzM6OkpXVxdtbW3MzMwwODjI1NQUhUKBwcFBjh8/zt69ewGYm5tjaGiI8fFxWlpa6O/vZ3x8nN7eXlZWVpifn19fZ0dHB319fUxMTNDX10c+n2dhYWF9eiaToaenh8nJSXbs2MHCwgKLi4vr0zs7O8lms0xPT7Nz505mZ2fJ5/Pr07PZLJlMhlwux8DAACdOnKCnpydQTGNjY3R3d8cW0/Hjx7nkkkvqxhSmnaKI6fjx4wwODoq2UxQxjY6OMj09HVvfCxpTPp9neno61r4XJKZ8Ps/c3FysfS9ITPPz8+zbt6/BVFuTyPIwWC5OQt/p7Owkl8vFmreCxrSyssL09HRseStoTAsLC+Tz+Vj7XpCYpqenOXjwYOx1QNpycRhaCoVCzRmcc+8Gbtnw9hjwh977x5xzrcAJ7/2mM0DxVt5/AXq89+91zl0PDHnvv+Wcuw74nPf+peXLHDlypHDo0KFQQcDaTi7t8CSjxRP0uJqnLOYpS8nz6NGjDx8+fPiaRtbR7DwMlouTgnnKYp7yaHGdm5vj8ccfD5yH635Jz3t/h/f+heV/QA4o/WZfD/DcxuWcc53AN4rT/3Xx7Z8Af1tc7/2sXcVoCSJaj9nZWYnVRI4WT9Djap6ymKcsEp5a8jCcX+3SDMxTFvOUR4trWM9GH7F4ALgReAh4A3Bf+cRisv1b4B7v/Z+VTfo4MAnc5py7grUrHrUvYQckn89LrCZytHiCHlfzlMU8ZYnQM3F5GKxdpDFPWcxTHi2uYT0bLZC/CHzNOXc/kAfeCuCcuxV4EmgDXgVsc869objMh4FPA3c6524CloF3NLj9TaR1HL440eJqnrKYpywReiYuD4O1izTmKYt5yqPFdXh4mMnJycDzN1Qge+/PAG+u8P7tZS+rjadxUyPbrMfo6Cj79++PYtWiaPEEPa7mKYt5yhKVZxLzMFi7SGOespinPFpcw46DnJofCknrMCNxosXVPGUxT1m0eEqhJV7zlMU8ZdHiCXpcw3qmpkDOZDJxKwRCiyfocTVPWcxTFi2eUmiJ1zxlMU9ZtHiCHtewnqkpkHO5XNwKgdDiCXpczVMW85RFi6cUWuI1T1nMUxYtnqDHNaxnagrkgYGBuBUCocUT9LiapyzmKYsWTym0xGuespinLFo8QY9rWM/UFMhp/QQTJ1pczVMW85RFi6cUWuI1T1nMUxYtnqDH9by9gry0tBS3QiC0eIIeV/OUxTxl0eIphZZ4zVMW85RFiyfocQ3rmZoCWdM4fFrQ4mqespinLFo8pdASr3nKYp6yaPEEPa5hPVNTIIcd3y4utHiCHlfzlMU8ZdHiKYWWeM1TFvOURYsn6HE9b8dB7urqilshEFo8QY+recpinrJo8ZRCS7zmKYt5yqLFE/S4hvVMTYHc1tYWt0IgtHiCHlfzlMU8ZdHiKYWWeM1TFvOURYsn6HEN65maAnlmZiZuhUBo8QQ9ruYpi3nKosVTCi3xmqcs5imLFk/Q4xrWMzUF8uDgYNwKgdDiCXpczVMW85RFi6cUWuI1T1nMUxYtnqDHNaxnagrkqampuBUCocUT9LiapyzmKYsWTym0xGuespinLFo8QY9rWM/UFMiFQiFuhUBo8QQ9ruYpi3nKosVTCi3xmqcs5imLFk/Q4xrWMzUFclov8ceJFlfzlMU8ZdHiKYWWeM1TFvOURYsn6HE9bx+xGBsbi1shEFo8QY+recpinrJo8ZRCS7zmKYt5yqLFE/S4hvVMTYHc3d0dt0IgtHiCHlfzlMU8ZdHiKYWWeM1TFvOURYsn6HEN65maAtkwDMMwDMMwJEhNgTw3Nxe3QiC0eIIeV/OUxTxl0eIphZZ4zVMW85RFiyfocQ3rmZoCeWhoKG6FQGjxBD2u5imLecqixVMKLfGapyzmKYsWT9DjGtYzNQXy+Ph43AqB0OIJelzNUxbzlEWLpxRa4jVPWcxTFi2eoMc1rGdqCuSWlpa4FQKhxRP0uJqnLOYpixZPKbTEa56ymKcsWjxBj2tYz/ZGNuKcywJ3AruAWeDt3vvxDfN8HnhFcTrAG4F8veUapb+/X2I1kaPFE/S4mqcs5ilLVJ5JzMNg7SKNecpinvJoce3v7+f06dOB52/0CvL7gWPe+1cCXwc+WmGeq4Df9t7fUPzLBVyuIdJ6iT9OtLiapyzmKUuEnonLw2DtIo15ymKe8mhxbdYjFtcB3y3+/x3gteUTnXOtwEHgS865B5xz7wqy3Fbo7e2VWlWkaPEEPa7mKYt5yhKhZ+LyMFi7SGOespinPFpcw3rWfcTCOfdu4JYNb48BueL/s0DfhuldwBeA24E24F7n3E+A3jrLsby8zMjICAMDA+RyOZaWlhgeHmZ0dJSuri7a2tqYmZlhcHCQqakpCoUCg4ODjI6Osrq6CqwN5TE0NMT4+DgtLS309/czPj5Ob28vKysrzM/Pr6+zo6ODvr4+JiYm6OvrI5/Ps7CwsD49k8nQ09PD5OQkO3bsYGFhgcXFxfXpnZ2dZLNZpqen2blzJ7Ozs+Tz+fXp2WyWTCZDLpdjYGCA8fFxZmZmAsU0Nja2PrB1HDGV1lUvpjDtFFVMi4uLou0URUzT09PntH2z+17QmErvxdn3gsTU1tbGwsJC7H2vXkxLS0u0tbXVS7U1aXYeBsvFSeg7nZ2dnD17lpmZmdjyVtCYMpkMIyMjseWtoDEVCoXY+16QmObm5shms7HXAWnLxWFoKRQKoRYAcM59G/i09/4h51wf8ID3/oVl09uA7d772eLr24BjwO/WWg7gyJEjhUOHDoV2GhkZYf/+/aGXazZaPEGPq3nKYp6ylDyPHj368OHDh6+RWm+UeRgsFycF85TFPOXR4joyMsLk5GTgPNzoIxYPADcW/38DcN+G6ZcD9zvn2pxzHazd0jsaYLmGGR4ellpVpGjxBD2u5imLecoSoWfi8jBYu0hjnrKYpzxaXMN6NlogfxH4J865+4E/AD4B4Jy71Tn3O977x4BvAA8CPwS+7r3/ebXlJBgdHZVaVaRo8QQ9ruYpi3nKEqFn4vIwWLtIY56ymKc8WlzDejY0zJv3/gzw5grv3172/23AbUGWk6CjoyOK1YqjxRP0uJqnLOYpS1SeSczDYO0ijXnKYp7yaHEN65maHwrp66v4PZPEocUT9LiapyzmKYsWTym0xGuespinLFo8QY9rWM/UFMgTExNxKwRCiyfocTVPWcxTFi2eUmiJ1zxlMU9ZtHiCHtewnqkpkNP6CSZOtLiapyzmKYsWTym0xGuespinLFo8QY/reXsFOZ/Px60QCC2eoMfVPGUxT1m0eEqhJV7zlMU8ZdHiCXpcw3qmpkBeWFiIWyEQWjxBj6t5ymKesmjxlEJLvOYpi3nKosUT9LiG9UxNgZzWcfjiRIurecpinrJo8ZRCS7zmKYt5yqLFE/S4Nmsc5MSR1nH44kSLq3nKYp6yaPGUQku85imLecqixRP0uIb1TE2BnMlk4lYIhBZP0ONqnrKYpyxaPKXQEq95ymKesmjxBD2uYT1TUyD39PTErRAILZ6gx9U8ZTFPWbR4SqElXvOUxTxl0eIJelzDeqamQJ6cnIxbIRBaPEGPq3nKYp6yaPGUQku85imLecqixRP0uIb1TE2BvGPHjrgVAqHFE/S4mqcs5imLFk8ptMRrnrKYpyxaPEGPa1jP1BTIaR1mJE60uJqnLOYpixZPKbTEa56ymKcsWjxBj+t5O8zb4uJi3AqB0OIJelzNUxbzlEWLpxRa4jVPWcxTFi2eoMc1rGdqCuS0jsMXJ1pczVMW85RFi6cUWuI1T1nMUxYtnqDH1cZBTjhaPEGPq3nKYp6yaPGUQku85imLecqixRP0uJ634yB3dnbGrRAILZ6gx9U8ZTFPWbR4SqElXvOUxTxl0eIJelzDeqamQM5ms3ErBEKLJ+hxNU9ZzFMWLZ5SaInXPGUxT1m0eIIe17CeqSmQp6en41YIhBZP0ONqnrKYpyxaPKXQEq95ymKesmjxBD2uYT1TUyDv3LkzboVAaPEEPa7mKYt5yqLFUwot8ZqnLOYpixZP0OMa1jM1BfLs7GzcCoHQ4gl6XM1TFvOURYunFFriNU9ZzFMWLZ6gxzWsZ2oK5Hw+H7dCILR4gh5X85TFPGXR4imFlnjNUxbzlEWLJ+hxDeuZmgI5rePwxYkWV/OUxTxl0eIphZZ4zVMW85RFiyfocQ3r2d7IRpxzWeBOYBcwC7zdez9eNv0lwJ+XLXIt8Cbge8AzwBPF94947z/ciMNGRkdH2b9/v8SqIkWLJ+hxNU9ZzFOWqDyTmIfB2kUa85TFPOXR4hp2HOSGCmTg/cAx7/2fOOfeAnwU+EBpovf+UeAGAOfcm4Fnvfffdc5dBhz13t/c4HarktZhRuJEi6t5ymKeskTombg8DNYu0pinLOYpjxbXsJ6NFsjXAbcV//8O8LFKMznnuoBPANcX37oa2OucuxdYAG7x3vsGHc4hk8lIrCZytHiCHlfzlMU8ZYnQM3F5GKxdpDFPWcxTHi2uYT3rFsjOuXcDt2x4ewzIFf+fBfqqLP5u4Fve+4ni61PAp7z333LOXcfa7cGXli+wvLzMyMgIAwMD5HI5lpaWGB4eZnR0lK6uLtra2piZmWFwcJCpqSkKhQKDg4OcOHGClZUVAObm5hgaGmJ8fJyWlhb6+/sZHx+nt7eXlZUV5ufn19fZ0dFBX18fExMT9PX1kc/nWVhYWJ+eyWTo6elhcnKSHTt2sLCwwOLi4vr0zs5Ostks09PT7Ny5k9nZWfL5/Pr0bDZLJpMhl8sxMDDAM888Qy6XCxTT2NgY3d3dscV04sQJ2tvb68YUpp2iiOnEiRMsLCyItlMUMY2Ojp7T9s3ue0Fjyufz5HK5WPtekJjy+TxnzpyJte8FiWl+fp7W1q193aPZeRgsFyeh73R2dpLL5cjlcrHlraAxrayskMvlYstbQWNaWFhgeXk51r4XJKbp6Wk6OztjrwPSlovD0FIoFEItAOCc+zbwae/9Q865PuAB7/0LK8z3D8A/996fLL7eDix77/PF188Ce7336xJHjhwpHDp0KLTT/Pw8XV1doZdrNlo8QY+recpinrKUPI8ePfrw4cOHr5Fab5R5GCwXJwXzlMU85dHiOj8/j/c+cB5u9LLGA8CNxf/fANy3cYZiwt5WSspFPg78UXH6FcCJjUm5UXK5XP2ZEoAWT9Djap6ymKcsEXomLg+DtYs05imLecqjxTWsZ6PPIH8R+Jpz7n4gD7wVwDl3K/Ck9/7vgMuB4xuW+zRwp3PuJmAZeEeD29/E0tKS1KoiRYsn6HE1T1nMU5YIPROXh8HaRRrzlMU85dHiGtazoUcsoqTR23pnz55l27ZtERjJosUT9LiapyzmKUvJU/oRi6ixXJwMzFMW85RHi+vZs2f5+c9/HvkjFokj7Ph2caHFE/S4mqcs5imLFk8ptMRrnrKYpyxaPEGPa1jP1BTIGh4QBz2eoMfVPGUxT1m0eEqhJV7zlMU8ZdHiCXpcw3qmpkBua2uLWyEQWjxBj6t5ymKesmjxlEJLvOYpi3nKosUT9LiG9UxNgTwzMxO3QiC0eIIeV/OUxTxl0eIphZZ4zVMW85RFiyfocQ3rmZoCeXBwMG6FQGjxBD2u5imLecqixVMKLfGapyzmKYsWT9DjGtYzNQXy1NRU3AqB0OIJelzNUxbzlEWLpxRa4jVPWcxTFi2eoMc1rGdqCuSkDVdXDS2eoMfVPGUxT1m0eEqhJV7zlMU8ZdHiCXpcw3qmpkBO6yX+ONHiap6ymKcsWjyl0BKvecpinrJo8QQ9ruftIxZjY2NxKwRCiyfocTVPWcxTFi2eUmiJ1zxlMU9ZtHiCHtewnqkpkLu7u+NWCIQWT9Djap6ymKcsWjyl0BKvecpinrJo8QQ9rmE9U1MgG4ZhGIZhGIYEqSmQ5+bm4lYIhBZP0ONqnrKYpyxaPKXQEq95ymKesmjxBD2uYT1TUyAPDQ3FrRAILZ6gx9U8ZTFPWbR4SqElXvOUxTxl0eIJelzDeqamQB4fH49bIRBaPEGPq3nKYp6yaPGUQku85imLecqixRP0uIb1TE2B3NLSErdCILR4gh5X85TFPGXR4imFlnjNUxbzlEWLJ+hxDeuZmgK5v78/boVAaPEEPa7mKYt5yqLFUwot8ZqnLOYpixZP0OMa1jM1BXJaL/HHiRZX85TFPGXR4imFlnjNUxbzlEWLJ+hxPW8fsejt7Y1bIRBaPEGPq3nKYp6yaPGUQku85imLecqixRP0uIb1TE2BvLKyErdCILR4gh5X85TFPGXR4imFlnjNUxbzlEWLJ+hxDeuZmgJ5fn4+boVAaPEEPa7mKYt5yqLFUwot8ZqnLOYpixZP0OMa1jM1BfLw8HDcCoHQ4gl6XM1TFvOURYunFFriNU9ZzFMWLZ6gxzWsZ2oK5NHR0bgVAqHFE/S4mqcs5imLFk8ptMRrnrKYpyxaPEGPa1jP1BTIHR0dcSsEQosn6HE1T1nMUxYtnlJoidc8ZTFPWbR4gh7XsJ7tW9mYc+53gTd7799aYdp7gPcCy8Cfeu//r3MuC9wJ7AJmgbd770XGB+nr65NYTeRo8QQ9ruYpi3nKErVnkvIwWLtIY56ymKc8Wlz7+vpCXUVu+Aqyc+6/Ap+qtA7n3DDwb4FXAL8NfMo5tw14P3DMe/9K4OvARxvd/kYmJiakVhUpWjxBj6t5ymKeskTpmbQ8DNYu0pinLOYpjxbXsJ5buYL8Y+BvWLs6sZGXAQ94788CZ51zTwIvBq4DbivO8x3gYxsXPHPmzMTRo0dHGhGanJxsZLGmo8UT9LiapyzmKUvRc38Eq44kD4Pl4iRhnrKYpzyKXAPn4boFsnPu3cAtG95+p/f+m865G6os1gvkyl7PAn0b3i+9dw6HDx8erOdkGIZxPtHsPAyWiw3DOL+pWyB77+8A7gi53hmgp+x1D/DchvdL7xmGYRg1sDxsGIbRXLb0Jb0aPAT8Z+dcJ7AN+A3gZ8ADwI3F6W8A7oto+4ZhGOc7locNwzAaRLRAds7dCjzpvf8759znWUu8rcC/994vOue+CHzNOXc/kAc2fevaMAzDaBzLw4ZhGFunpVAoxO2wJZxzrcBfAFcAZ4Hf994/Ga9VZZxzj/D8s39Pe+/fGafPRpxzvwn8mff+BufcZcBXgQJrV53+jfd+NU6/Ehs8rwLuBp4oTv6i9/6b8dmBc64D+ApwgLUrd38K/IIE7s8qrs+QvH3aBnwZcMAK8E6ghYTt0yqefSRsf0qjKQ+D5WIpLBfLYHlYHolcHNUjFs3kTUCn9/7lzrlrgc8Cb4xXaTPF25x472+IWaUizrkPAm8DSj9WfjvwUe/9/3PO/SVr+/SuuPxKVPC8Crjde//Z+Kw28S+BSe/925xzO4FHgEdJ4P6ksut/JHn79GYA7/0ril9Ku521xJy0fVrJ826Stz+leRMK8jBYLpbCcrEolofl2XIuTsMv6V0HfBfAe/8gcE28OlW5AtjunPu+c+6e4kkkSTwF/LOy11cDPyz+/x3gtU03qkwlz5uccz9yzt3hnOupslwz+RbnDp21THL3ZzXXRO1T7/3fAH9QfLkfGCOB+7SGZ6L2ZwRoycNguVgKy8VyWB4WRiIXp6FA3jiU0YpzLolXxs8An2FtwP73Ad9Ikqf3/v8AS2VvtXjvS8/fVB0KqtlU8HwI+GPv/fXAr4CPxyJWhvd+zns/Wzz4/jdrP8SQ1P1ZyTVx+xTAe7/snPsa8AXWXJO6Tzd6JnJ/CqMlD4PlYhEsF8theTgatpqL01AgbxzKqNV7vxyXTA0eB+703he8948Dk8DumJ1qUf4MUZKHgrrLe/9w6X/gyjhlSjjnLgTuBf7Ke//XJHh/VnBN5D4F8N6/HbictWfLsmWTErVPN3h+P6n7UxAteRgsF0dFIvOGllxseTgatpKL01Agl4Ysonir7Fi8OlV5F2vP5eGc28PaFZdTsRrV5pGyHyBI8lBQ33POvaz4/2Hg4VozNwPn3BDwfeBD3vuvFN9O5P6s4prEffo259yHiy/PsHaS+0nS9mkVz28nbX9GgJY8DJaLoyKJeUNFLrY8LI9ELk7MbaUtcBfwOufcj1l7WDxR30Yu4w7gq8WhlQrAuxJ8hQXg3wFfds5lgMdYuz2RRN4P/DfnXB4Y5flnjuLkI8AO4GPOudJzZR8APp/A/VnJ9VbgzxO2T78N/A/n3I+ADuCPWNuPSeujlTxPkrw+Ko2WPAyWi6PCcnHjWB6WZ8u5WP0wb4ZhGIZhGIYhSRoesTAMwzAMwzAMMaxANgzDMAzDMIwyrEA2DMMwDMMwjDKsQDYMwzAMwzCMMqxANgzDMAzDMIwyrEA2DMMwDMMwjDKsQDYMwzAMwzCMMv4/X8ZBzFiGOEQAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 720x576 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "tsplot(SP500_adj_price1d)\n",
    "plt.savefig('Figure5.png')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8347f7cf",
   "metadata": {},
   "source": [
    "**Also, the first-difference stock data shows how the daily changes of S\\&P 500 stock price change along the timeline, which would be useful for us to explore the relationship between sentiment score changes shown in the USTR tweets, press releases and WSJ newspapers and the first-difference stock price.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "7ab42a39",
   "metadata": {},
   "outputs": [],
   "source": [
    "df2_4 = df2_3.copy()\n",
    "df2_4 = df2_4.iloc[1:,:]\n",
    "df2_4['SP500_adj_price1d'] = SP500_adj_price1d"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "563b3fed",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>SP500_adj_price</th>\n",
       "      <th>SP500_volume</th>\n",
       "      <th>SP500_adj_price1d</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2018-01-03</th>\n",
       "      <td>2713.060059</td>\n",
       "      <td>3.544030e+09</td>\n",
       "      <td>17.250000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-04</th>\n",
       "      <td>2723.989990</td>\n",
       "      <td>3.697340e+09</td>\n",
       "      <td>10.929931</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-05</th>\n",
       "      <td>2743.149902</td>\n",
       "      <td>3.239280e+09</td>\n",
       "      <td>19.159912</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-06</th>\n",
       "      <td>2743.149902</td>\n",
       "      <td>3.239280e+09</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-07</th>\n",
       "      <td>2743.149902</td>\n",
       "      <td>3.239280e+09</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            SP500_adj_price  SP500_volume  SP500_adj_price1d\n",
       "Date                                                        \n",
       "2018-01-03      2713.060059  3.544030e+09          17.250000\n",
       "2018-01-04      2723.989990  3.697340e+09          10.929931\n",
       "2018-01-05      2743.149902  3.239280e+09          19.159912\n",
       "2018-01-06      2743.149902  3.239280e+09           0.000000\n",
       "2018-01-07      2743.149902  3.239280e+09           0.000000"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2_4.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d72db010",
   "metadata": {},
   "source": [
    "# Data Aggregation & Model Building"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "891fecb9",
   "metadata": {},
   "outputs": [],
   "source": [
    "df1 = df1.set_index('Date')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "e93d4368",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>SP500_adj_price</th>\n",
       "      <th>SP500_volume</th>\n",
       "      <th>SP500_adj_price1d</th>\n",
       "      <th>NewsTitle</th>\n",
       "      <th>NewsText</th>\n",
       "      <th>VADER_Newspaper_Negative</th>\n",
       "      <th>VADER_Newspaper_Positive</th>\n",
       "      <th>VADER_Newspaper_Neutral</th>\n",
       "      <th>VADER_Newspaper_Compound</th>\n",
       "      <th>TextBlob_Newspaper_Sentiment_Polarity</th>\n",
       "      <th>TextBlob_Newspaper_Sentiment_Subjectivity</th>\n",
       "      <th>VADER_Newspaper_Positive_Sentiment</th>\n",
       "      <th>TextBlob_Newspaper_Positive_Sentiment</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2018-01-03</th>\n",
       "      <td>2713.060059</td>\n",
       "      <td>3.544030e+09</td>\n",
       "      <td>17.250000</td>\n",
       "      <td>The Hunt for Centuries-Old Books Reveals the P...</td>\n",
       "      <td>digit databas centuries-old book dawn print sh...</td>\n",
       "      <td>0.015</td>\n",
       "      <td>0.045</td>\n",
       "      <td>0.940</td>\n",
       "      <td>0.9042</td>\n",
       "      <td>0.058201</td>\n",
       "      <td>0.276989</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-03</th>\n",
       "      <td>2713.060059</td>\n",
       "      <td>3.544030e+09</td>\n",
       "      <td>17.250000</td>\n",
       "      <td>Foreign Firms Rush Washers, Solar Panels Into ...</td>\n",
       "      <td>foreign maker product includ wash machin solar...</td>\n",
       "      <td>0.069</td>\n",
       "      <td>0.107</td>\n",
       "      <td>0.824</td>\n",
       "      <td>0.9823</td>\n",
       "      <td>0.047174</td>\n",
       "      <td>0.388386</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-03</th>\n",
       "      <td>2713.060059</td>\n",
       "      <td>3.544030e+09</td>\n",
       "      <td>17.250000</td>\n",
       "      <td>Readers Take Alan Blinder to Task on Taxes</td>\n",
       "      <td>alan blinder' \"almost everyth wrong new tax la...</td>\n",
       "      <td>0.124</td>\n",
       "      <td>0.202</td>\n",
       "      <td>0.675</td>\n",
       "      <td>0.9515</td>\n",
       "      <td>0.133141</td>\n",
       "      <td>0.451253</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-03</th>\n",
       "      <td>2713.060059</td>\n",
       "      <td>3.544030e+09</td>\n",
       "      <td>17.250000</td>\n",
       "      <td>China's Yuan Setting Is Highest Since May 2016...</td>\n",
       "      <td>china' central bank guid yuan highest level u....</td>\n",
       "      <td>0.037</td>\n",
       "      <td>0.124</td>\n",
       "      <td>0.838</td>\n",
       "      <td>0.9799</td>\n",
       "      <td>0.077356</td>\n",
       "      <td>0.342184</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-04</th>\n",
       "      <td>2723.989990</td>\n",
       "      <td>3.697340e+09</td>\n",
       "      <td>10.929931</td>\n",
       "      <td>Finance &amp;amp; Markets: Yuan Fix at Strongest S...</td>\n",
       "      <td>china' central bank guid yuan highest level u....</td>\n",
       "      <td>0.047</td>\n",
       "      <td>0.108</td>\n",
       "      <td>0.845</td>\n",
       "      <td>0.9493</td>\n",
       "      <td>0.070952</td>\n",
       "      <td>0.338164</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            SP500_adj_price  SP500_volume  SP500_adj_price1d  \\\n",
       "Date                                                           \n",
       "2018-01-03      2713.060059  3.544030e+09          17.250000   \n",
       "2018-01-03      2713.060059  3.544030e+09          17.250000   \n",
       "2018-01-03      2713.060059  3.544030e+09          17.250000   \n",
       "2018-01-03      2713.060059  3.544030e+09          17.250000   \n",
       "2018-01-04      2723.989990  3.697340e+09          10.929931   \n",
       "\n",
       "                                                    NewsTitle  \\\n",
       "Date                                                            \n",
       "2018-01-03  The Hunt for Centuries-Old Books Reveals the P...   \n",
       "2018-01-03  Foreign Firms Rush Washers, Solar Panels Into ...   \n",
       "2018-01-03         Readers Take Alan Blinder to Task on Taxes   \n",
       "2018-01-03  China's Yuan Setting Is Highest Since May 2016...   \n",
       "2018-01-04  Finance &amp; Markets: Yuan Fix at Strongest S...   \n",
       "\n",
       "                                                     NewsText  \\\n",
       "Date                                                            \n",
       "2018-01-03  digit databas centuries-old book dawn print sh...   \n",
       "2018-01-03  foreign maker product includ wash machin solar...   \n",
       "2018-01-03  alan blinder' \"almost everyth wrong new tax la...   \n",
       "2018-01-03  china' central bank guid yuan highest level u....   \n",
       "2018-01-04  china' central bank guid yuan highest level u....   \n",
       "\n",
       "            VADER_Newspaper_Negative  VADER_Newspaper_Positive  \\\n",
       "Date                                                             \n",
       "2018-01-03                     0.015                     0.045   \n",
       "2018-01-03                     0.069                     0.107   \n",
       "2018-01-03                     0.124                     0.202   \n",
       "2018-01-03                     0.037                     0.124   \n",
       "2018-01-04                     0.047                     0.108   \n",
       "\n",
       "            VADER_Newspaper_Neutral  VADER_Newspaper_Compound  \\\n",
       "Date                                                            \n",
       "2018-01-03                    0.940                    0.9042   \n",
       "2018-01-03                    0.824                    0.9823   \n",
       "2018-01-03                    0.675                    0.9515   \n",
       "2018-01-03                    0.838                    0.9799   \n",
       "2018-01-04                    0.845                    0.9493   \n",
       "\n",
       "            TextBlob_Newspaper_Sentiment_Polarity  \\\n",
       "Date                                                \n",
       "2018-01-03                               0.058201   \n",
       "2018-01-03                               0.047174   \n",
       "2018-01-03                               0.133141   \n",
       "2018-01-03                               0.077356   \n",
       "2018-01-04                               0.070952   \n",
       "\n",
       "            TextBlob_Newspaper_Sentiment_Subjectivity  \\\n",
       "Date                                                    \n",
       "2018-01-03                                   0.276989   \n",
       "2018-01-03                                   0.388386   \n",
       "2018-01-03                                   0.451253   \n",
       "2018-01-03                                   0.342184   \n",
       "2018-01-04                                   0.338164   \n",
       "\n",
       "            VADER_Newspaper_Positive_Sentiment  \\\n",
       "Date                                             \n",
       "2018-01-03                                 1.0   \n",
       "2018-01-03                                 1.0   \n",
       "2018-01-03                                 1.0   \n",
       "2018-01-03                                 1.0   \n",
       "2018-01-04                                 1.0   \n",
       "\n",
       "            TextBlob_Newspaper_Positive_Sentiment  \n",
       "Date                                               \n",
       "2018-01-03                                    1.0  \n",
       "2018-01-03                                    1.0  \n",
       "2018-01-03                                    1.0  \n",
       "2018-01-03                                    1.0  \n",
       "2018-01-04                                    1.0  "
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2_5 = pd.merge(df2_4,df1, how='left', left_index=True, right_index=True)\n",
    "df2_5.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "37dce8df",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "DatetimeIndex: 8111 entries, 2018-01-03 to 2022-10-31\n",
      "Data columns (total 13 columns):\n",
      " #   Column                                     Non-Null Count  Dtype  \n",
      "---  ------                                     --------------  -----  \n",
      " 0   SP500_adj_price                            8111 non-null   float64\n",
      " 1   SP500_volume                               8111 non-null   float64\n",
      " 2   SP500_adj_price1d                          8111 non-null   float64\n",
      " 3   NewsTitle                                  5862 non-null   object \n",
      " 4   NewsText                                   7638 non-null   object \n",
      " 5   VADER_Newspaper_Negative                   7638 non-null   float64\n",
      " 6   VADER_Newspaper_Positive                   7638 non-null   float64\n",
      " 7   VADER_Newspaper_Neutral                    7638 non-null   float64\n",
      " 8   VADER_Newspaper_Compound                   7638 non-null   float64\n",
      " 9   TextBlob_Newspaper_Sentiment_Polarity      7638 non-null   float64\n",
      " 10  TextBlob_Newspaper_Sentiment_Subjectivity  7638 non-null   float64\n",
      " 11  VADER_Newspaper_Positive_Sentiment         7638 non-null   float64\n",
      " 12  TextBlob_Newspaper_Positive_Sentiment      7638 non-null   float64\n",
      "dtypes: float64(11), object(2)\n",
      "memory usage: 887.1+ KB\n"
     ]
    }
   ],
   "source": [
    "df2_5.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b39cbd1c",
   "metadata": {},
   "source": [
    "## Exploring Stock Price vs. Newspaper Sentiment"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "6650de2b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>OLS Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>     <td>SP500_adj_price</td> <th>  R-squared:         </th> <td>   0.033</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared:    </th> <td>   0.033</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th> <td>   52.40</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>             <td>Fri, 02 Dec 2022</td> <th>  Prob (F-statistic):</th> <td>1.24e-53</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                 <td>23:04:46</td>     <th>  Log-Likelihood:    </th> <td> -60543.</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Observations:</th>      <td>  7638</td>      <th>  AIC:               </th> <td>1.211e+05</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Residuals:</th>          <td>  7632</td>      <th>  BIC:               </th> <td>1.211e+05</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Model:</th>              <td>     5</td>      <th>                     </th>     <td> </td>    \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Covariance Type:</th>      <td>nonrobust</td>    <th>                     </th>     <td> </td>    \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "                    <td></td>                       <th>coef</th>     <th>std err</th>      <th>t</th>      <th>P>|t|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>const</th>                                 <td>-2667.7727</td> <td> 1.52e+04</td> <td>   -0.176</td> <td> 0.861</td> <td>-3.25e+04</td> <td> 2.71e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>VADER_Newspaper_Negative</th>              <td> 3847.2926</td> <td> 1.52e+04</td> <td>    0.253</td> <td> 0.800</td> <td>-2.59e+04</td> <td> 3.36e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>VADER_Newspaper_Positive</th>              <td> 5502.1120</td> <td> 1.52e+04</td> <td>    0.362</td> <td> 0.717</td> <td>-2.43e+04</td> <td> 3.53e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>VADER_Newspaper_Neutral</th>               <td> 6514.1618</td> <td> 1.52e+04</td> <td>    0.429</td> <td> 0.668</td> <td>-2.33e+04</td> <td> 3.63e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>VADER_Newspaper_Compound</th>              <td>   -8.7922</td> <td>   16.885</td> <td>   -0.521</td> <td> 0.603</td> <td>  -41.891</td> <td>   24.307</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>TextBlob_Newspaper_Sentiment_Polarity</th> <td>  764.2328</td> <td>  120.917</td> <td>    6.320</td> <td> 0.000</td> <td>  527.202</td> <td> 1001.263</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Omnibus:</th>       <td>16088.751</td> <th>  Durbin-Watson:     </th> <td>   0.063</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Omnibus):</th>  <td> 0.000</td>   <th>  Jarque-Bera (JB):  </th> <td> 524.716</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Skew:</th>           <td> 0.077</td>   <th>  Prob(JB):          </th> <td>1.15e-114</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Kurtosis:</th>       <td> 1.725</td>   <th>  Cond. No.          </th> <td>5.39e+03</td> \n",
       "</tr>\n",
       "</table><br/><br/>Notes:<br/>[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.<br/>[2] The condition number is large, 5.39e+03. This might indicate that there are<br/>strong multicollinearity or other numerical problems."
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                            OLS Regression Results                            \n",
       "==============================================================================\n",
       "Dep. Variable:        SP500_adj_price   R-squared:                       0.033\n",
       "Model:                            OLS   Adj. R-squared:                  0.033\n",
       "Method:                 Least Squares   F-statistic:                     52.40\n",
       "Date:                Fri, 02 Dec 2022   Prob (F-statistic):           1.24e-53\n",
       "Time:                        23:04:46   Log-Likelihood:                -60543.\n",
       "No. Observations:                7638   AIC:                         1.211e+05\n",
       "Df Residuals:                    7632   BIC:                         1.211e+05\n",
       "Df Model:                           5                                         \n",
       "Covariance Type:            nonrobust                                         \n",
       "=========================================================================================================\n",
       "                                            coef    std err          t      P>|t|      [0.025      0.975]\n",
       "---------------------------------------------------------------------------------------------------------\n",
       "const                                 -2667.7727   1.52e+04     -0.176      0.861   -3.25e+04    2.71e+04\n",
       "VADER_Newspaper_Negative               3847.2926   1.52e+04      0.253      0.800   -2.59e+04    3.36e+04\n",
       "VADER_Newspaper_Positive               5502.1120   1.52e+04      0.362      0.717   -2.43e+04    3.53e+04\n",
       "VADER_Newspaper_Neutral                6514.1618   1.52e+04      0.429      0.668   -2.33e+04    3.63e+04\n",
       "VADER_Newspaper_Compound                 -8.7922     16.885     -0.521      0.603     -41.891      24.307\n",
       "TextBlob_Newspaper_Sentiment_Polarity   764.2328    120.917      6.320      0.000     527.202    1001.263\n",
       "==============================================================================\n",
       "Omnibus:                    16088.751   Durbin-Watson:                   0.063\n",
       "Prob(Omnibus):                  0.000   Jarque-Bera (JB):              524.716\n",
       "Skew:                           0.077   Prob(JB):                    1.15e-114\n",
       "Kurtosis:                       1.725   Cond. No.                     5.39e+03\n",
       "==============================================================================\n",
       "\n",
       "Notes:\n",
       "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
       "[2] The condition number is large, 5.39e+03. This might indicate that there are\n",
       "strong multicollinearity or other numerical problems.\n",
       "\"\"\""
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#creating OLS regression using statsmodels API\n",
    "X1 = sm.add_constant(df2_5[['VADER_Newspaper_Negative', 'VADER_Newspaper_Positive', 'VADER_Newspaper_Neutral', 'VADER_Newspaper_Compound', 'TextBlob_Newspaper_Sentiment_Polarity']])\n",
    "lr_model = sm.OLS(df2_5['SP500_adj_price'], X1, missing='drop').fit()\n",
    "# summarize our model\n",
    "lr_model.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1d136354",
   "metadata": {},
   "source": [
    "**Based on the summary of linear regression, the R squared value is nearly 0.03, leading to the conclusion that the linear regression model is not a good model representing the relationship between the stock price and the sentiment scores. Next, I will utilize NLP to explore the accuracy of using news articles content to predict the changes of stock data changes.**"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "46422284",
   "metadata": {},
   "source": [
    "## Exploring Stock Price vs Newspaper Text"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "d367dfe3",
   "metadata": {},
   "outputs": [],
   "source": [
    "df2_5['SP500_adj_price_increase'] = np.where(df2_5['SP500_adj_price1d'] > 0, 1, 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "871d5824",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SP500_adj_price_increase\n",
       "0    4977\n",
       "1    3134\n",
       "Name: SP500_adj_price_increase, dtype: int64"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2_6 = df2_5['SP500_adj_price_increase'].groupby(df2_5['SP500_adj_price_increase']).count()\n",
    "df2_6"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "e5ffb26a",
   "metadata": {},
   "outputs": [],
   "source": [
    "df2_7 = df2_5[['NewsText', 'SP500_adj_price_increase']]\n",
    "df2_7 = df2_7.dropna()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "5a6ed48c",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Split data into training and test sets\n",
    "X_train, X_test, y_train, y_test = train_test_split(df2_7['NewsText'], \n",
    "                                                    df2_7['SP500_adj_price_increase'],\n",
    "                                                    test_size = 0.25,\n",
    "                                                    random_state=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7ace2dc3",
   "metadata": {},
   "source": [
    "## CountVectorizer\n",
    "Bag of words"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "665ac00f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-3 {color: black;background-color: white;}#sk-container-id-3 pre{padding: 0;}#sk-container-id-3 div.sk-toggleable {background-color: white;}#sk-container-id-3 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-3 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-3 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-3 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-3 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-3 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-3 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-3 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-3 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-3 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-3 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-3 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-3 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-3 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-3 div.sk-item {position: relative;z-index: 1;}#sk-container-id-3 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-3 div.sk-item::before, #sk-container-id-3 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-3 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-3 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-3 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-3 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-3 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-3 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-3 div.sk-label-container {text-align: center;}#sk-container-id-3 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-3 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-3\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>CountVectorizer()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-3\" type=\"checkbox\" checked><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">CountVectorizer</label><div class=\"sk-toggleable__content\"><pre>CountVectorizer()</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "CountVectorizer()"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.feature_extraction.text import CountVectorizer\n",
    "\n",
    "# Fit the CountVectorizer to the training data\n",
    "vect = CountVectorizer().fit(X_train)\n",
    "vect"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "9de99dd3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['____________________________________________________________',\n",
       "       'anatoliy', 'backdrop', 'blinking', 'calkins', 'civilisation',\n",
       "       'cornromania', 'definitely', 'dotis', 'entrepreneurial', 'filmed',\n",
       "       'gazzal', 'halle', 'huerta', 'intertwined', 'khawar', 'lifea',\n",
       "       'marland', 'mitski', 'newmark', 'oti', 'photocopi', 'projectil',\n",
       "       'refer', 'rounding', 'senzel', 'sofi', 'subsoil', 'therapy',\n",
       "       'ulrich', 'versicherung', 'wolrath'], dtype=object)"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vect.get_feature_names_out()[::2000]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "a03e5844",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "63286"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(vect.get_feature_names())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "338d4668",
   "metadata": {},
   "outputs": [],
   "source": [
    "# transform the documents in the training and testing data to a document-term matrix\n",
    "X_train_vectorized = vect.transform(X_train)\n",
    "X_test_vectorized = vect.transform(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "0686521b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-4 {color: black;background-color: white;}#sk-container-id-4 pre{padding: 0;}#sk-container-id-4 div.sk-toggleable {background-color: white;}#sk-container-id-4 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-4 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-4 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-4 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-4 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-4 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-4 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-4 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-4 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-4 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-4 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-4 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-4 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-4 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-4 div.sk-item {position: relative;z-index: 1;}#sk-container-id-4 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-4 div.sk-item::before, #sk-container-id-4 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-4 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-4 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-4 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-4 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-4 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-4 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-4 div.sk-label-container {text-align: center;}#sk-container-id-4 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-4 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-4\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LogisticRegression(max_iter=1000)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-4\" type=\"checkbox\" checked><label for=\"sk-estimator-id-4\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LogisticRegression</label><div class=\"sk-toggleable__content\"><pre>LogisticRegression(max_iter=1000)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "LogisticRegression(max_iter=1000)"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Train the linear regression model\n",
    "model = LogisticRegression(max_iter = 1000)\n",
    "model.fit(X_train_vectorized, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "970c54cc",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "AUC:  0.5551544622425629\n"
     ]
    }
   ],
   "source": [
    "# Predict the transformed test documents\n",
    "predictions = model.predict(X_test_vectorized)\n",
    "print('AUC: ', roc_auc_score(y_test, predictions))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "ee6e5b9e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0.00083302,  0.01340121, -0.04756453, ..., -0.00642717,\n",
       "        -0.00223206, -0.00023458]])"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.coef_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "2f9aea49",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Smallest Coefs:\n",
      "['manufacturers' 'normal' 'smartphon' 'friday' 'collar' 'eventu' 'enorm'\n",
      " 'off' 'common' 'kept']\n",
      "\n",
      "Largest Coefs:\n",
      "['recommend' 'figures' 'likely' 'blow' 'consumers' 'suffer' 'settl'\n",
      " 'eager' 'sarah' 'depress']\n"
     ]
    }
   ],
   "source": [
    "# get the feature names as numpy array\n",
    "feature_names = np.array(vect.get_feature_names_out())\n",
    "\n",
    "# Sort the coefficients from the model\n",
    "sorted_coef_index = model.coef_[0].argsort()\n",
    "\n",
    "# Find the 10 smallest and 10 largest coefficients\n",
    "# The 10 largest coefficients are being indexed using [:-11:-1] \n",
    "# so the list returned is in order of largest to smallest\n",
    "print('Smallest Coefs:\\n{}\\n'.format(feature_names[sorted_coef_index[:10]]))\n",
    "print('Largest Coefs:\\n{}'.format(feature_names[sorted_coef_index[:-11:-1]]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "da566f9f",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Logistic Regression roc_auc : 0.5551544622425629, score_train : 0.9998254189944135 and score_test : 0.5795811518324607\n",
      "KNN roc_auc : 0.516687643020595, score_train : 0.7018156424581006 and score_test : 0.5633507853403141\n",
      "Decision Tree roc_auc : 0.5440961098398169, score_train : 0.9998254189944135 and score_test : 0.5649214659685864\n",
      "Random Forest roc_auc : 0.5307265446224256, score_train : 0.9998254189944135 and score_test : 0.6151832460732984\n",
      "SGD Classifier roc_auc : 0.5503203661327231, score_train : 0.9928421787709497 and score_test : 0.5659685863874345\n",
      "Naive Bayes roc_auc : 0.5750457665903891, score_train : 0.7758379888268156 and score_test : 0.5780104712041885\n",
      "SVM Classifier roc_auc : 0.5502288329519451, score_train : 0.9996508379888268 and score_test : 0.5701570680628272\n"
     ]
    }
   ],
   "source": [
    "# define models to train\n",
    "names = [\"Logistic Regression\", \"KNN\", \"Decision Tree\", \"Random Forest\", \"SGD Classifier\", \"Naive Bayes\", \"SVM Classifier\"]\n",
    "\n",
    "classifiers = [\n",
    "    LogisticRegression(max_iter = 10000),\n",
    "    KNeighborsClassifier(),\n",
    "    DecisionTreeClassifier(),\n",
    "    RandomForestClassifier(),\n",
    "    SGDClassifier(max_iter = 100),\n",
    "    MultinomialNB(),\n",
    "    SVC(kernel='linear')\n",
    "]\n",
    "\n",
    "models = zip(names, classifiers)\n",
    "\n",
    "for name, model in models:\n",
    "    model.fit(X_train_vectorized, y_train)\n",
    "    roc_auc = roc_auc_score(y_test, model.predict(X_test_vectorized))\n",
    "    score_train = model.score(X_train_vectorized, y_train)    \n",
    "    score_test = model.score(X_test_vectorized, y_test)\n",
    "    print(\"{} roc_auc : {}, score_train : {} and score_test : {}\".format(name, roc_auc, score_train, score_test))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6104c2f2",
   "metadata": {},
   "source": [
    "**Through ROC AUC value, the Naïve Bayes model performs best, with 0.5750 ROC AUC value. \n",
    "Based on testing accuracy, random forest performs best, with 0.6099 accuracy.**"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6fc6759a",
   "metadata": {},
   "source": [
    "## Tfidf\n",
    "Vector instead of bag of words"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "66e38c80",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "19836"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.feature_extraction.text import TfidfVectorizer\n",
    "\n",
    "# Fit the TfidfVectorizer to the training data specifiying a minimum document frequency of 5\n",
    "tf_vect = TfidfVectorizer(min_df = 5).fit(X_train)\n",
    "len(tf_vect.get_feature_names_out())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "d8bbfc57",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "AUC:  0.5428775743707094\n"
     ]
    }
   ],
   "source": [
    "X_train_vect_tf = tf_vect.transform(X_train)\n",
    "\n",
    "X_test_vect_tf = tf_vect.transform(X_test)\n",
    "\n",
    "tf_model = LogisticRegression()\n",
    "\n",
    "tf_model.fit(X_train_vect_tf, y_train)\n",
    "\n",
    "tf_predictions = tf_model.predict(X_test_vect_tf)\n",
    "\n",
    "print('AUC: ', roc_auc_score(y_test, tf_predictions))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "6669d975",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Smallest tfidf:\n",
      "['reprint' 'abt' 'spotti' 'shadowi' 'interlock' 'flexion' 'intolerable'\n",
      " 'immort' 'trilogy' 'somervil']\n",
      "\n",
      "Largest tfidf: \n",
      "['bitcoin' 'honda' 'porch' 'vw' 'guild' 'wine' 'canola' 'tomato' 'shultz'\n",
      " 'vodka']\n"
     ]
    }
   ],
   "source": [
    "feature_names = np.array(tf_vect.get_feature_names_out())\n",
    "\n",
    "sorted_tfidf_index = X_train_vect_tf.max(0).toarray()[0].argsort()\n",
    "\n",
    "print('Smallest tfidf:\\n{}\\n'.format(feature_names[sorted_tfidf_index[:10]]))\n",
    "print('Largest tfidf: \\n{}'.format(feature_names[sorted_tfidf_index[:-11:-1]]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "id": "c3a5828f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Smallest Coefs:\n",
      "['friday' 'saturday' 'sunday' 'don' 'wsj' 'dr' 'eventu' 'war' 'normal'\n",
      " 'economist']\n",
      "\n",
      "Largest Coefs: \n",
      "['monday' 'tuesday' 'wednesday' 'financi' 'recommend' 'republican'\n",
      " 'retail' 'educ' 'ethanol' 'energi']\n"
     ]
    }
   ],
   "source": [
    "sorted_coef_index = tf_model.coef_[0].argsort()\n",
    "\n",
    "print('Smallest Coefs:\\n{}\\n'.format(feature_names[sorted_coef_index[:10]]))\n",
    "print('Largest Coefs: \\n{}'.format(feature_names[sorted_coef_index[:-11:-1]]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "id": "df5c8d4d",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Logistic Regression roc_auc : 0.5428775743707094, score_train : 0.7702513966480447 and score_test : 0.6104712041884817\n",
      "KNN roc_auc : 0.5540217391304348, score_train : 0.7280027932960894 and score_test : 0.5905759162303665\n",
      "Decision Tree roc_auc : 0.5269508009153319, score_train : 0.9998254189944135 and score_test : 0.5429319371727749\n",
      "Random Forest roc_auc : 0.5344450800915331, score_train : 0.9998254189944135 and score_test : 0.6083769633507854\n",
      "SGD Classifier roc_auc : 0.5614759725400458, score_train : 0.8962988826815642 and score_test : 0.6068062827225131\n",
      "Naive Bayes roc_auc : 0.5044164759725401, score_train : 0.6230796089385475 and score_test : 0.6041884816753926\n",
      "SVM Classifier roc_auc : 0.55354118993135, score_train : 0.8355446927374302 and score_test : 0.612565445026178\n"
     ]
    }
   ],
   "source": [
    "# define models to train\n",
    "names = [\"Logistic Regression\", \"KNN\", \"Decision Tree\", \"Random Forest\", \"SGD Classifier\", \"Naive Bayes\", \"SVM Classifier\"]\n",
    "\n",
    "classifiers = [\n",
    "    LogisticRegression(max_iter = 10000),\n",
    "    KNeighborsClassifier(),\n",
    "    DecisionTreeClassifier(),\n",
    "    RandomForestClassifier(),\n",
    "    SGDClassifier(max_iter = 100),\n",
    "    MultinomialNB(),\n",
    "    SVC(kernel='linear')\n",
    "]\n",
    "\n",
    "models = zip(names, classifiers)\n",
    "\n",
    "for name, model in models:\n",
    "    model.fit(X_train_vect_tf, y_train)\n",
    "    roc_auc = roc_auc_score(y_test, model.predict(X_test_vect_tf))\n",
    "    score_train = model.score(X_train_vect_tf, y_train)    \n",
    "    score_test = model.score(X_test_vect_tf, y_test)\n",
    "    print(\"{} roc_auc : {}, score_train : {} and score_test : {}\".format(name, roc_auc, score_train, score_test))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "578de69a",
   "metadata": {},
   "source": [
    "**Through ROC AUC value, the Stochastic Gradient Descent (SGD) model performs best, with 0.5643 ROC AUC value. \n",
    "Based on testing accuracy, Support Vector Machine (SVM) performs best, with 0.6126 accuracy.**"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1781e687",
   "metadata": {},
   "source": [
    "## n-grams"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "b06594e2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "92979"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Fit the CountVectorizer to the training data specifiying a minimum \n",
    "# document frequency of 5 and extracting 1-grams and 2-grams\n",
    "vect_ngrams = CountVectorizer(min_df=5, ngram_range=(1,2)).fit(X_train)\n",
    "\n",
    "X_train_vectorized_ngrams = vect_ngrams.transform(X_train)\n",
    "\n",
    "len(vect_ngrams.get_feature_names())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "id": "f8f567ab",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "AUC:  0.569324942791762\n"
     ]
    }
   ],
   "source": [
    "model = LogisticRegression()\n",
    "model.fit(X_train_vectorized_ngrams, y_train)\n",
    "\n",
    "X_test_vectorized_ngrams = vect_ngrams.transform(X_test)\n",
    "predictions = model.predict(X_test_vectorized_ngrams)\n",
    "\n",
    "print('AUC: ', roc_auc_score(y_test, predictions))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "id": "78de122b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Smallest n_grams:\n",
      "['cola' 'abil see' 'confrontations' 'compani requir' 'blizzard' 'ban ship'\n",
      " 'bloomberg barclay' 'biggest issu' 'council group' 'concern suppli']\n",
      "\n",
      "Largest n_grams: \n",
      "['aggress polici' 'biden choic' 'china retali' 'crisis accord'\n",
      " 'beijing belt' 'current covid' 'allow fed' 'could end'\n",
      " 'company headquart' 'crimean peninsula']\n",
      "Smallest Coefs:\n",
      "['friday' 'normal' 'eventu' 'kept' 'manufacturers' 'weekli' 'smartphon'\n",
      " 'common' 'off' 'healthi']\n",
      "\n",
      "Largest Coefs: \n",
      "['recommend' 'monday' 'educ' 'assist' 'emphas' 'urg' 'sarah' 'blow'\n",
      " 'depress' 'monetari']\n"
     ]
    }
   ],
   "source": [
    "feature_names = np.array(vect_ngrams.get_feature_names())\n",
    "\n",
    "sorted_ngram_index = X_train_vectorized_ngrams.max(0).toarray()[0].argsort()\n",
    "\n",
    "print('Smallest n_grams:\\n{}\\n'.format(feature_names[sorted_tfidf_index[:10]]))\n",
    "print('Largest n_grams: \\n{}'.format(feature_names[sorted_tfidf_index[:-11:-1]]))\n",
    "\n",
    "sorted_coef_index = model.coef_[0].argsort()\n",
    "\n",
    "print('Smallest Coefs:\\n{}\\n'.format(feature_names[sorted_coef_index[:10]]))\n",
    "print('Largest Coefs: \\n{}'.format(feature_names[sorted_coef_index[:-11:-1]]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "id": "6a087fe5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Logistic Regression roc_auc : 0.5632036613272311, score_train : 0.9998254189944135 and score_test : 0.5890052356020943\n",
      "KNN roc_auc : 0.5077860411899313, score_train : 0.6984986033519553 and score_test : 0.562303664921466\n",
      "Decision Tree roc_auc : 0.5468821510297482, score_train : 0.9998254189944135 and score_test : 0.5701570680628272\n",
      "Random Forest roc_auc : 0.5233009153318078, score_train : 0.9998254189944135 and score_test : 0.6078534031413613\n",
      "SGD Classifier roc_auc : 0.5597139588100686, score_train : 0.9956354748603352 and score_test : 0.5853403141361256\n",
      "Naive Bayes roc_auc : 0.5821567505720824, score_train : 0.7987081005586593 and score_test : 0.5895287958115183\n",
      "SVM Classifier roc_auc : 0.5619565217391305, score_train : 0.9996508379888268 and score_test : 0.5848167539267015\n"
     ]
    }
   ],
   "source": [
    "# define models to train\n",
    "names = [\"Logistic Regression\", \"KNN\", \"Decision Tree\", \"Random Forest\", \"SGD Classifier\", \"Naive Bayes\", \"SVM Classifier\"]\n",
    "\n",
    "classifiers = [\n",
    "    LogisticRegression(max_iter = 10000),\n",
    "    KNeighborsClassifier(),\n",
    "    DecisionTreeClassifier(),\n",
    "    RandomForestClassifier(),\n",
    "    SGDClassifier(max_iter = 100),\n",
    "    MultinomialNB(),\n",
    "    SVC(kernel='linear')\n",
    "]\n",
    "\n",
    "models = zip(names, classifiers)\n",
    "\n",
    "for name, model in models:\n",
    "    model.fit(X_train_vectorized_ngrams, y_train)\n",
    "    roc_auc = roc_auc_score(y_test, model.predict(X_test_vectorized_ngrams))\n",
    "    score_train = model.score(X_train_vectorized_ngrams, y_train)    \n",
    "    score_test = model.score(X_test_vectorized_ngrams, y_test)\n",
    "    print(\"{} roc_auc : {}, score_train : {} and score_test : {}\".format(name, roc_auc, score_train, score_test))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5f2445e4",
   "metadata": {},
   "source": [
    "**Through ROC AUC value, the Naive Bayes model performs best, with 0.5822 ROC AUC value. \n",
    "Based on testing accuracy, Random Forest performs best, with 0.6079 accuracy.**"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d74d02c4",
   "metadata": {},
   "source": [
    "**In conclusion, Naïve Bayes model is the best model according to ROC AUC value and Random Forest model has the best performance according to testing score. Besides, there is no big difference on the model accuracy utilizing different vectorizers to generate the matrix of token counts.**\n",
    "\n",
    "**We could try to use NLP analysis of WSJ newspaper articles related to U.S. trade to predict the changes of stock price.**"
   ]
  }
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