3_Notebook_UserDescription_TopicModelling.ipynb

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 "cells": [
  {
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   "cell_type": "markdown",
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   "source": [
    "# Topic Modelling on User Description"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The present notebook is part of a series of notebooks related to the MSc. thesis: **Sentiment analysis on generative language models    based on Social Media commentary of industry participants**\n",
    "\n",
    "The MSc. thesis research was conducted based on tweets about ChatGPT. These were collected, processed and analyzed with the scope of answering the following research question:\n",
    "\n",
    "**How are generative language models perceived by participants of different industries based on social media commentary?**\n",
    "\n",
    "A focal point in answering the research question consists of identifying the industries the users discussing ChatGPT belong to. In this sense, this notebook was used to further clean the user description data and to extract the topics of these descriptions with the help of unsupervised machine learning techniques sucha as: Latent Dirichlet Allocation. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
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    {
     "name": "stderr",
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     "text": [
      "c:\\Users\\oanaa\\anaconda3\\envs\\CodeBase\\lib\\site-packages\\numpy\\_distributor_init.py:30: UserWarning: loaded more than 1 DLL from .libs:\n",
      "c:\\Users\\oanaa\\anaconda3\\envs\\CodeBase\\lib\\site-packages\\numpy\\.libs\\libopenblas.FB5AE2TYXYH2IJRDKGDGQ3XBKLKTF43H.gfortran-win_amd64.dll\n",
      "c:\\Users\\oanaa\\anaconda3\\envs\\CodeBase\\lib\\site-packages\\numpy\\.libs\\libopenblas64__v0.3.21-gcc_10_3_0.dll\n",
      "  warnings.warn(\"loaded more than 1 DLL from .libs:\"\n",
      "c:\\Users\\oanaa\\anaconda3\\envs\\CodeBase\\lib\\site-packages\\past\\builtins\\misc.py:45: DeprecationWarning: the imp module is deprecated in favour of importlib and slated for removal in Python 3.12; see the module's documentation for alternative uses\n",
      "  from imp import reload\n"
     ]
    }
   ],
   "source": [
    "#Import cell of necessary packages\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "#Set pandas max rows option as max 500 for ease of cleaning\n",
    "pd.set_option('display.max_rows', 500)\n",
    "pd.options.mode.chained_assignment = None\n",
    "\n",
    "#Package to surpass long warnings\n",
    "import warnings\n",
    "warnings.simplefilter(action='ignore')\n",
    "warnings.filterwarnings(\"ignore\", category = DeprecationWarning) \n",
    "\n",
    "#Method from functools package that compares data based on key in dict\n",
    "from functools import cmp_to_key #comparing function \n",
    "\n",
    "#Import module to ease data transformation for LDA models\n",
    "from gensim.corpora.dictionary import Dictionary # Import the dictionary builder\n",
    "#Import LDA module\n",
    "from gensim.models import LdaMulticore # we'll use the faster multicore version of LDA\n",
    "#Importing module to calculate coherence of LDA models\n",
    "from gensim.models import CoherenceModel\n",
    "#Import modules to be able to save LDA models\n",
    "from gensim.test.utils import datapath\n",
    "from gensim import  models\n",
    "#Package used in fitting corpus for LDA topic modelling\n",
    "import nltk.corpus\n",
    "#Packages and models used to visualise LDA model results\n",
    "import pyLDAvis\n",
    "import pyLDAvis.gensim_models as gensimvis\n",
    "\n",
    "#Packages used for data visualisation \n",
    "from PIL import Image\n",
    "from wordcloud import WordCloud, STOPWORDS, ImageColorGenerator\n",
    "from matplotlib import pyplot as plt"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Data Preprocessing"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this section of the notebook the *user description dataset* is further pre-processed before the LDA model is trained on the user descriptions. \n",
    "\n",
    "This section has been run multiple times based on the observations drawn from the results of the LDA model. \n",
    "To increase the LDA model's coherence and the interpretabality of the resulting topics, words that were considered redundant were removed from the corpus of the model. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
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       "                 usr_userid  \\\n",
       "698719   832624291724144640   \n",
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       "\n",
       "                                   usr_description_origin  \\\n",
       "698719  coder interested in #ai #ML #sentience #health...   \n",
       "698720  Founder @PplPolicyProj: patreon.com/peoplespol...   \n",
       "698721              enjoyer of long walks in the dungeon.   \n",
       "\n",
       "       usr_description_prepro             usr_description_hashtags  \\\n",
       "698719                    NaN  ['ai', 'ML', 'sentience', 'health']   \n",
       "698720                    NaN                                   []   \n",
       "698721                    NaN                                   []   \n",
       "\n",
       "             usr_description_mentions  \\\n",
       "698719                             []   \n",
       "698720  ['PplPolicyProj', 'ebruenig']   \n",
       "698721                             []   \n",
       "\n",
       "                                     usr_description_URLs  \\\n",
       "698719                                                 []   \n",
       "698720  ['patreon.com/peoplespolicyp….', 'patreon.com/...   \n",
       "698721                                                 []   \n",
       "\n",
       "       usr_description_emoji  \\\n",
       "698719                   NaN   \n",
       "698720                   NaN   \n",
       "698721                   NaN   \n",
       "\n",
       "                                    usr_description_clean  \\\n",
       "698719  coder interested in of extensant citizen initi...   \n",
       "698720            founder   co-host of the bruenigs with    \n",
       "698721               enjoyer of long walks in the dungeon   \n",
       "\n",
       "                                   usr_description_tokens  \n",
       "698719  [extensant, initiative, interested, citizen, c...  \n",
       "698720                    [host, -, co, founder, bruenig]  \n",
       "698721                     [enjoyer, dungeon, walk, long]  "
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     "execution_count": 2,
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   ],
   "source": [
    "#Load user description data\n",
    "user_prepro_df = pd.read_csv('data_files/UsrData_PreLDA.csv')\n",
    "\n",
    "#transform usr_description_tokens column into lists\n",
    "for col in ['usr_description_tokens']:\n",
    "    user_prepro_df[f'{col}'] = user_prepro_df[f'{col}'].apply(lambda x: x.strip(\"']['\").split(\"', '\"))\n",
    "\n",
    "#inspect how dataframe looks like \n",
    "user_prepro_df.tail(3)"
   ]
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   "execution_count": 3,
   "metadata": {},
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    "user_prepro_df.shape"
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       "                 usr_userid usr_description_origin usr_description_prepro  \\\n",
       "698705           2469116083                    NaN                    NaN   \n",
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       "\n",
       "       usr_description_hashtags usr_description_mentions usr_description_URLs  \\\n",
       "698705                       []                       []                   []   \n",
       "698706                       []                       []                   []   \n",
       "698707                       []                       []                   []   \n",
       "698708                       []                       []                   []   \n",
       "698709                       []                       []                   []   \n",
       "\n",
       "       usr_description_emoji usr_description_clean usr_description_tokens  \n",
       "698705                   NaN                   NaN                  [nan]  \n",
       "698706                   NaN                   NaN                  [nan]  \n",
       "698707                   NaN                   NaN                  [nan]  \n",
       "698708                   NaN                   NaN                  [nan]  \n",
       "698709                   NaN                   NaN                  [nan]  "
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     "execution_count": 3,
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   ],
   "source": [
    "#Inspect how users with no description look like \n",
    "user_prepro_df[user_prepro_df[\"usr_description_origin\"].isna()].tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "#extra_stop_words list stores other words that were observed in the corpus and are considered noise \n",
    "extra_stop_words = ['r','rt','an','dm','le','yo','por','I','el','lo','con','x','u',\n",
    "                    '',\"\",\"-\",\"also\" \"k\",\"c\",\"oth\",\"rth\",\"0\",'da',\n",
    "                    \"e\", \"la\", \"prev\",\"e\", \"k\",\"et\",\"un\", str(np.nan), 'nan', 'NaN'                    \n",
    "                    'dad','hehim','mom',\n",
    "                    'father','husband','sheh','que','god','man','woman','married',\n",
    "                    'christian','guy','alway','always','person','vida','friend','stuff',\n",
    "                    'never','bear','son','eth','fuck','they','them','she','her',\n",
    "                    'je','wife','mother','jesus','b','st','I','l','sheher','non',\n",
    "                    'hehim','theythem','theythem','shehim','shethem','son','daughter',\n",
    "                    'brother','sister','them',\"thing\", \"random\",\"much\",\"many\",\"de\",\"sometimes\",] "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [],
   "source": [
    "#removal of words considered as noise\n",
    "user_prepro_df[f'usr_description_tokens'] = user_prepro_df[f'usr_description_tokens'].apply(lambda x: x if type(x) == type(None) else list(set(x).difference(extra_stop_words)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Create a usr_industry_categ column meant to store the results of the topic modelling conducted on user descriptions\n",
    "user_prepro_df['urs_industry_categ'] = \"\"\n",
    "#Users that do not have an industry description or any user description tokens will be assigned the \"NotSpecified\" category \n",
    "user_prepro_df['urs_industry_categ'] = user_prepro_df['usr_description_tokens'].apply(lambda x: \"NotSpecified\" if len(x)==0 else \"\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
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       "\n",
       "                                   usr_description_prepro  \\\n",
       "315392  Plagiarist, utilizer, curious about nature, cy...   \n",
       "455667                          i don’t support Trump2024   \n",
       "464363                                                 🇺🇸   \n",
       "615751                                                NaN   \n",
       "615752                                                NaN   \n",
       "\n",
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       "315392                   NaN   \n",
       "455667                   NaN   \n",
       "464363                    🇺🇸   \n",
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       "615752                   NaN   \n",
       "\n",
       "                                    usr_description_clean  \\\n",
       "315392  plagiarist utilizer curious about nature cynic...   \n",
       "455667                               i dont support trump   \n",
       "464363                                                NaN   \n",
       "615751                                                NaN   \n",
       "615752                                                NaN   \n",
       "\n",
       "                                   usr_description_tokens urs_industry_categ  \n",
       "315392  [curious, plagiarist, multispecular, fascinate...                     \n",
       "455667                                   [trump, support]                     \n",
       "464363                                                 []       NotSpecified  \n",
       "615751                                                 []       NotSpecified  \n",
       "615752                                                 []       NotSpecified  "
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Inspect how data looks like once the new column is added\n",
    "user_prepro_df[user_prepro_df[\"usr_description_origin\"].isna()].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "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>usr_userid</th>\n",
       "      <th>usr_description_origin</th>\n",
       "      <th>usr_description_prepro</th>\n",
       "      <th>usr_description_hashtags</th>\n",
       "      <th>usr_description_mentions</th>\n",
       "      <th>usr_description_URLs</th>\n",
       "      <th>usr_description_emoji</th>\n",
       "      <th>usr_description_clean</th>\n",
       "      <th>usr_description_tokens</th>\n",
       "      <th>urs_industry_categ</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>38374100</td>\n",
       "      <td>#Cloudtechnology / #web3 / #softwareengineerin...</td>\n",
       "      <td>#Cloudtechnology / #web3 / #softwareengineerin...</td>\n",
       "      <td>['Cloudtechnology', 'web3', 'softwareengineeri...</td>\n",
       "      <td>[]</td>\n",
       "      <td>[]</td>\n",
       "      <td>NaN</td>\n",
       "      <td></td>\n",
       "      <td>[]</td>\n",
       "      <td>NotSpecified</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>1605</td>\n",
       "      <td>@OpenAI</td>\n",
       "      <td>@OpenAI</td>\n",
       "      <td>[]</td>\n",
       "      <td>['OpenAI']</td>\n",
       "      <td>[]</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[]</td>\n",
       "      <td>NotSpecified</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>92</th>\n",
       "      <td>4691850570</td>\n",
       "      <td>🐕🎡🎠🍵🌻🌈🌌🦄</td>\n",
       "      <td>🐕🎡🎠🍵🌻🌈🌌🦄</td>\n",
       "      <td>[]</td>\n",
       "      <td>[]</td>\n",
       "      <td>[]</td>\n",
       "      <td>🐕🎡🎠🍵🌻🌈🌌🦄</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[]</td>\n",
       "      <td>NotSpecified</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>127</th>\n",
       "      <td>1442574755006087171</td>\n",
       "      <td>🤠\\n\\ngiphy.com/channel/shellw…</td>\n",
       "      <td>🤠\\n\\ngiphy.com/channel/shellw…</td>\n",
       "      <td>[]</td>\n",
       "      <td>[]</td>\n",
       "      <td>['giphy.com/channel/shellw…']</td>\n",
       "      <td>🤠</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[]</td>\n",
       "      <td>NotSpecified</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>169</th>\n",
       "      <td>1272995053691076608</td>\n",
       "      <td>I’m a person</td>\n",
       "      <td>I’m a person</td>\n",
       "      <td>[]</td>\n",
       "      <td>[]</td>\n",
       "      <td>[]</td>\n",
       "      <td>NaN</td>\n",
       "      <td>im a person</td>\n",
       "      <td>[]</td>\n",
       "      <td>NotSpecified</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              usr_userid                             usr_description_origin  \\\n",
       "12              38374100  #Cloudtechnology / #web3 / #softwareengineerin...   \n",
       "41                  1605                                            @OpenAI   \n",
       "92            4691850570                                           🐕🎡🎠🍵🌻🌈🌌🦄   \n",
       "127  1442574755006087171                     🤠\\n\\ngiphy.com/channel/shellw…   \n",
       "169  1272995053691076608                                       I’m a person   \n",
       "\n",
       "                                usr_description_prepro  \\\n",
       "12   #Cloudtechnology / #web3 / #softwareengineerin...   \n",
       "41                                             @OpenAI   \n",
       "92                                            🐕🎡🎠🍵🌻🌈🌌🦄   \n",
       "127                     🤠\\n\\ngiphy.com/channel/shellw…   \n",
       "169                                       I’m a person   \n",
       "\n",
       "                              usr_description_hashtags  \\\n",
       "12   ['Cloudtechnology', 'web3', 'softwareengineeri...   \n",
       "41                                                  []   \n",
       "92                                                  []   \n",
       "127                                                 []   \n",
       "169                                                 []   \n",
       "\n",
       "    usr_description_mentions           usr_description_URLs  \\\n",
       "12                        []                             []   \n",
       "41                ['OpenAI']                             []   \n",
       "92                        []                             []   \n",
       "127                       []  ['giphy.com/channel/shellw…']   \n",
       "169                       []                             []   \n",
       "\n",
       "    usr_description_emoji usr_description_clean usr_description_tokens  \\\n",
       "12                    NaN                                           []   \n",
       "41                    NaN                   NaN                     []   \n",
       "92               🐕🎡🎠🍵🌻🌈🌌🦄                   NaN                     []   \n",
       "127                     🤠                   NaN                     []   \n",
       "169                   NaN           im a person                     []   \n",
       "\n",
       "    urs_industry_categ  \n",
       "12        NotSpecified  \n",
       "41        NotSpecified  \n",
       "92        NotSpecified  \n",
       "127       NotSpecified  \n",
       "169       NotSpecified  "
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Inspect how the rows being categorised as 'NotSpecified' look like\n",
    "user_prepro_df[user_prepro_df[\"urs_industry_categ\"] == \"NotSpecified\"].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(105065, 10)"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Check no. of users with NotSpecified industry \n",
    "user_prepro_df[user_prepro_df[\"urs_industry_categ\"]==\"NotSpecified\"].shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(593657, 10)"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Select the data that is not \"NotSpecified\" as introduction \n",
    "topic_df = user_prepro_df[user_prepro_df[\"urs_industry_categ\"] != \"NotSpecified\"]\n",
    "topic_df.shape"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Users' Description LDA "
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To infer the industry to which Twitter users belong, the unsupervised machine learning model (Latent Dirichlet Allocation) has been used. \n",
    "LDA is a generative probabilistic model which employs unsupervised learning techniques to find clusters of similar topics among the words provided in the documents used as input data (Blei et al., 2003).\n",
    "\n",
    "To fit an LDA model on the descriptions of users, this paper made used of tha **LDAMulticore** model of the **gensim** python package. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Create a dictionary of the words present in the usr_description column \n",
    "dictionary = Dictionary(topic_df['usr_description_tokens'])\n",
    "#filter out low-frequency / high-frequency words\n",
    "dictionary.filter_extremes(no_below=10, no_above=0.5)\n",
    "#create corpus from the usr_descriptions_tokens column\n",
    "corpus = [dictionary.doc2bow(doc) for doc in topic_df['usr_description_tokens']]"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A first step in fitting and LDA model to the users' description data was to instantiate a base model that will be further tuned for better results.\n",
    "The model will be assesed based on its coherence score that serves as an indicator to how interpretable the topics are to human readers. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Fit a based LDA model with 12 topics\n",
    "lda_model12 = LdaMulticore(corpus, id2word=dictionary, num_topics=12, workers = 4, passes=10, iterations= 25)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Coherence Score 12 Topics:  0.5118806133667163\n"
     ]
    }
   ],
   "source": [
    "#Check coherence score of base model \n",
    "coherence_model_lda12 = CoherenceModel(model=lda_model12, texts=topic_df['usr_description_tokens'], dictionary=dictionary, coherence='c_v')\n",
    "coherence_lda12 = coherence_model_lda12.get_coherence()\n",
    "print('Coherence Score 12 Topics: ', coherence_lda12)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "##SaveUSR LDA Model\n",
    "lda_model12.save('pre_TunedUserLDA/userLDA_PRE_Tuned')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "<link rel=\"stylesheet\" type=\"text/css\" href=\"https://cdn.jsdelivr.net/gh/bmabey/pyLDAvis@3.3.1/pyLDAvis/js/ldavis.v1.0.0.css\">\n",
       "\n",
       "\n",
       "<div id=\"ldavis_el182026810058247522519050774\"></div>\n",
       "<script type=\"text/javascript\">\n",
       "\n",
       "var ldavis_el182026810058247522519050774_data = {\"mdsDat\": {\"x\": [0.22460560214300643, 0.2162158483196118, -0.07235615423812296, -0.28821436739412387, -0.0925334054001199, 0.23531119477119058, -0.17881744892765578, -0.05877765825316941, -0.009672182088613687, 0.0439184811317977, -0.01440822652982992, -0.005271683533970353], \"y\": [-0.1443207152058603, 0.04881774410040142, -0.25025689527082334, -0.1430749440734375, 0.13061199636496526, -0.033269745746813705, 0.08769358183253652, -0.143383493192473, 0.13691054459103977, -0.10769638369503855, 0.225272818425211, 0.1926954918702928], \"topics\": [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12], \"cluster\": [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], \"Freq\": [12.853672249374359, 10.75809561867373, 9.501038840454578, 9.279261217546544, 9.168720943008903, 9.083025945454388, 8.256429726517183, 7.804466253153694, 6.9370747379846875, 6.441005636826097, 5.050443533870276, 4.866765297135565]}, \"tinfo\": {\"Term\": [\"engineer\", \"software\", 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\"year\", \"live\", \"world\", \"lover\", \"father\", \"husband\", \"dad\", \"proud\", \"travel\", \"family\", \"dog\", \"mom\", \"girl\", \"retire\", \"wife\", \"christian\", \"veteran\", \"reader\", \"amateur\", \"boy\", \"jesus\", \"son\", \"car\", \"mother\", \"avid\", \"marry\", \"fanatic\", \"junkie\", \"runner\", \"traveler\", \"army\", \"christ\", \"beer\", \"food\", \"coffee\", \"geek\", \"addict\", \"kid\", \"god\", \"cat\", \"love\", \"nerd\", \"sport\", \"fan\", \"enthusiast\", \"gamer\", \"tech\", \"music\", \"photographer\", \"life\", \"writer\", \"engineer\", \"software\", \"developer\", \"designer\", \"dev\", \"code\", \"stack\", \"cloud\", \"architect\", \"graphic\", \"python\", \"frontend\", \"indie\", \"hacker\", \"javascript\", \"react\", \"programming\", \"front\", \"coder\", \"io\", \"devop\", \"application\", \"ux\", \"android\", \"linux\", \"illustrator\", \"java\", \"core\", \"aw\", \"js\", \"app\", \"programmer\", \"web\", \"full\", \"open\", \"design\", 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\"opinion\", \"opinion\", \"option\", \"organic\", \"pa\", \"para\", \"part\", \"partner\", \"partner\", \"party\", \"peace\", \"people\", \"people\", \"people\", \"perfect\", \"personal\", \"personal\", \"personal\", \"personal\", \"pfp\", \"phd\", \"philosopher\", \"photographer\", \"photographer\", \"photographer\", \"photographer\", \"physicist\", \"plant\", \"platform\", \"platform\", \"platform\", \"platform\", \"play\", \"player\", \"player\", \"podcast\", \"podcast\", \"podcast\", \"podcast\", \"podcast\", \"podcast\", \"poet\", \"policy\", \"political\", \"political\", \"politics\", \"politics\", \"politics\", \"politics\", \"positive\", \"post\", \"post\", \"post\", \"post\", \"post\", \"private\", \"pro\", \"pro\", \"problem\", \"problem\", \"producer\", \"product\", \"product\", \"prof\", \"professor\", \"programmer\", \"programmer\", \"programming\", \"progress\", \"progress\", \"progress\", \"pronoun\", \"pronoun\", \"proud\", \"public\", \"public\", \"public\", \"python\", \"que\", \"question\", \"react\", \"reader\", \"ready\", \"real\", \"red\", \"relate\", \"religion\", \"reply\", \"reporter\", \"reporter\", \"research\", \"research\", \"researcher\", \"retire\", \"retweet\", \"review\", \"right\", \"right\", \"runner\", \"sale\", \"say\", \"scale\", \"school\", \"science\", \"science\", \"scientist\", \"scientist\", \"se\", \"sea\", \"security\", \"security\", \"security\", \"see\", \"self\", \"self\", \"send\", \"sense\", \"seo\", \"service\", \"shall\", \"share\", \"share\", \"share\", \"share\", \"sheh\", \"sheh\", \"sheh\", \"sheher\", \"sheher\", \"sheher\", \"shit\", \"shit\", \"simple\", \"singer\", \"social\", \"social\", \"social\", \"society\", \"society\", \"society\", \"software\", \"solo\", \"solution\", \"solution\", \"solution\", \"solve\", \"son\", \"soon\", \"soul\", \"sound\", \"source\", \"source\", \"soy\", \"speak\", \"speak\", \"specialize\", \"speech\", \"sport\", \"sport\", \"sport\", \"sport\", \"stack\", \"stan\", \"star\", \"state\", \"state\", \"state\", \"state\", \"stay\", \"step\", \"still\", \"stock\", \"stop\", \"stop\", \"story\", \"story\", \"strategic\", \"strategist\", \"strategy\", \"stream\", \"streamer\", \"street\", \"strive\", \"student\", \"student\", \"study\", \"stuff\", \"stuff\", \"subscribe\", \"suck\", \"system\", \"system\", \"system\", \"system\", \"take\", \"take\", \"take\", \"take\", \"take\", \"talk\", \"talk\", \"talk\", \"talk\", \"talk\", \"talk\", \"te\", \"teacher\", \"teacher\", \"tech\", \"tech\", \"tech\", \"tech\", \"tech\", \"tech\", \"tech\", \"tech\", \"tech\", \"technology\", \"technology\", \"technology\", \"technology\", \"technology\", \"technology\", \"term\", \"theythem\", \"think\", \"think\", \"thinker\", \"thinker\", \"thought\", \"tiktok\", \"time\", \"time\", \"time\", \"tip\", \"tip\", \"tip\", \"todo\", \"together\", \"tomorrow\", \"topic\", \"trade\", \"trade\", \"trader\", \"trading\", \"transform\", \"transformation\", \"travel\", \"traveler\", \"trend\", \"true\", \"truth\", \"try\", \"try\", \"tweet\", \"tweet\", \"tweet\", \"tweet\", \"tweet\", \"tweet\", \"tweet\", \"tweet\", \"twitch\", \"twitter\", \"ukraine\", \"una\", \"unique\", \"unique\", \"united\", \"university\", \"update\", \"ux\", \"verify\", \"veteran\", \"video\", \"video\", \"video\", \"view\", \"view\", \"voice\", \"voice\", \"voice\", \"voice\", \"voice\", \"wait\", \"wake\", \"walk\", \"want\", \"want\", \"water\", \"water\", \"water\", \"way\", \"way\", \"way\", \"wealth\", \"web\", \"web\", \"web\", \"weekly\", \"weird\", \"welcome\", \"well\", \"well\", \"well\", \"white\", \"wife\", \"win\", \"wisdom\", \"wonder\", \"word\", \"word\", \"work\", \"work\", \"work\", \"work\", \"work\", \"work\", \"world\", \"world\", \"world\", \"write\", \"write\", \"write\", \"write\", \"writer\", \"writer\", \"writer\", \"writer\", \"writer\", \"year\", \"year\", \"year\", \"year\", \"year\", \"year\", \"year\", \"year\", \"youtube\", \"youtube\", \"youtube\"]}, \"R\": 30, \"lambda.step\": 0.01, \"plot.opts\": {\"xlab\": \"PC1\", \"ylab\": \"PC2\"}, \"topic.order\": [11, 4, 1, 6, 7, 12, 8, 9, 10, 2, 3, 5]};\n",
       "\n",
       "function LDAvis_load_lib(url, callback){\n",
       "  var s = document.createElement('script');\n",
       "  s.src = url;\n",
       "  s.async = true;\n",
       "  s.onreadystatechange = s.onload = callback;\n",
       "  s.onerror = function(){console.warn(\"failed to load library \" + url);};\n",
       "  document.getElementsByTagName(\"head\")[0].appendChild(s);\n",
       "}\n",
       "\n",
       "if(typeof(LDAvis) !== \"undefined\"){\n",
       "   // already loaded: just create the visualization\n",
       "   !function(LDAvis){\n",
       "       new LDAvis(\"#\" + \"ldavis_el182026810058247522519050774\", ldavis_el182026810058247522519050774_data);\n",
       "   }(LDAvis);\n",
       "}else if(typeof define === \"function\" && define.amd){\n",
       "   // require.js is available: use it to load d3/LDAvis\n",
       "   require.config({paths: {d3: \"https://d3js.org/d3.v5\"}});\n",
       "   require([\"d3\"], function(d3){\n",
       "      window.d3 = d3;\n",
       "      LDAvis_load_lib(\"https://cdn.jsdelivr.net/gh/bmabey/pyLDAvis@3.3.1/pyLDAvis/js/ldavis.v3.0.0.js\", function(){\n",
       "        new LDAvis(\"#\" + \"ldavis_el182026810058247522519050774\", ldavis_el182026810058247522519050774_data);\n",
       "      });\n",
       "    });\n",
       "}else{\n",
       "    // require.js not available: dynamically load d3 & LDAvis\n",
       "    LDAvis_load_lib(\"https://d3js.org/d3.v5.js\", function(){\n",
       "         LDAvis_load_lib(\"https://cdn.jsdelivr.net/gh/bmabey/pyLDAvis@3.3.1/pyLDAvis/js/ldavis.v3.0.0.js\", function(){\n",
       "                 new LDAvis(\"#\" + \"ldavis_el182026810058247522519050774\", ldavis_el182026810058247522519050774_data);\n",
       "            })\n",
       "         });\n",
       "}\n",
       "</script>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Visualize base LDA model \n",
    "lda_display12 = pyLDAvis.gensim_models.prepare(lda_model12, corpus, dictionary)\n",
    "pyLDAvis.display(lda_display12)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[(0,\n",
       "  '0.041*\"good\" + 0.040*\"founder\" + 0.031*\"make\" + 0.028*\"co\" + 0.026*\"world\" + 0.025*\"life\" + 0.018*\"work\" + 0.016*\"real\" + 0.016*\"people\" + 0.015*\"build\"'),\n",
       " (1,\n",
       "  '0.035*\"crypto\" + 0.028*\"investor\" + 0.022*\"financial\" + 0.018*\"market\" + 0.018*\"advice\" + 0.017*\"trader\" + 0.016*\"thought\" + 0.016*\"share\" + 0.014*\"money\" + 0.014*\"self\"'),\n",
       " (2,\n",
       "  '0.015*\"pro\" + 0.015*\"anti\" + 0.015*\"scientist\" + 0.014*\"data\" + 0.014*\"free\" + 0.013*\"datum\" + 0.011*\"analyst\" + 0.009*\"english\" + 0.009*\"streamer\" + 0.009*\"liberal\"'),\n",
       " (3,\n",
       "  '0.025*\"science\" + 0.024*\"view\" + 0.022*\"student\" + 0.019*\"phd\" + 0.017*\"research\" + 0.014*\"opinion\" + 0.013*\"professor\" + 0.013*\"university\" + 0.012*\"researcher\" + 0.011*\"director\"'),\n",
       " (4,\n",
       "  '0.043*\"nft\" + 0.017*\"que\" + 0.014*\"crypto\" + 0.012*\"collector\" + 0.012*\"web\" + 0.011*\"artist\" + 0.011*\"en\" + 0.011*\"curious\" + 0.010*\"blockchain\" + 0.009*\"discord\"'),\n",
       " (5,\n",
       "  '0.019*\"go\" + 0.016*\"know\" + 0.012*\"see\" + 0.011*\"man\" + 0.010*\"right\" + 0.010*\"think\" + 0.010*\"come\" + 0.010*\"mind\" + 0.010*\"love\" + 0.010*\"never\"'),\n",
       " (6,\n",
       "  '0.026*\"lover\" + 0.024*\"father\" + 0.022*\"husband\" + 0.020*\"dad\" + 0.020*\"love\" + 0.014*\"enthusiast\" + 0.013*\"proud\" + 0.013*\"fan\" + 0.012*\"travel\" + 0.011*\"family\"'),\n",
       " (7,\n",
       "  '0.028*\"art\" + 0.023*\"artist\" + 0.023*\"music\" + 0.023*\"game\" + 0.023*\"fan\" + 0.018*\"hehim\" + 0.015*\"like\" + 0.014*\"stuff\" + 0.012*\"video\" + 0.012*\"play\"'),\n",
       " (8,\n",
       "  '0.034*\"follow\" + 0.030*\"news\" + 0.028*\"twitter\" + 0.027*\"account\" + 0.022*\"time\" + 0.016*\"tweet\" + 0.015*\"new\" + 0.013*\"late\" + 0.012*\"get\" + 0.010*\"part\"'),\n",
       " (9,\n",
       "  '0.030*\"writer\" + 0.022*\"book\" + 0.022*\"author\" + 0.020*\"editor\" + 0.014*\"write\" + 0.014*\"social\" + 0.012*\"journalist\" + 0.011*\"sheh\" + 0.009*\"host\" + 0.009*\"story\"'),\n",
       " (10,\n",
       "  '0.033*\"business\" + 0.029*\"digital\" + 0.022*\"marketing\" + 0.020*\"help\" + 0.019*\"content\" + 0.015*\"medium\" + 0.012*\"creator\" + 0.011*\"founder\" + 0.011*\"brand\" + 0.011*\"build\"'),\n",
       " (11,\n",
       "  '0.052*\"engineer\" + 0.044*\"software\" + 0.043*\"developer\" + 0.032*\"web\" + 0.024*\"designer\" + 0.019*\"enthusiast\" + 0.017*\"dev\" + 0.016*\"tech\" + 0.016*\"code\" + 0.015*\"design\"')]"
      ]
     },
     "execution_count": 116,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Print topic clusters resulted from base LDA model \n",
    "lda_model12.print_topics()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Create dictionary where keys are the index of topic clusters resulted from base LDA and values are potetntial naming convention of these clusers\n",
    "lda_dict = {0:'Entrepreneurship',\n",
    "            1:'Crypto & Finance',\n",
    "            2:'NotClear',\n",
    "            3:'Education & Academia',\n",
    "            4:'Crypto & Web 4.0',\n",
    "            5:'NotClear',\n",
    "            6:'NotClear',\n",
    "            7:'Arts & Entertainment',\n",
    "            8:'News & Commentary',\n",
    "            9:'Writers',\n",
    "            10:'Business & Marketing',\n",
    "            11:'Software Development',\n",
    "            '-':'-'}\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Defining a classifier function to use for labelling each user with an industry \n",
    "def classifier(texts,model,topics_dict):\n",
    "\n",
    "  #get probabilities list for each observation in list of tuples format [(cluster_no, probability)...]\n",
    "  texts_clean = texts.apply(lambda t: model[dictionary.doc2bow(t)])\n",
    "\n",
    "  # First: sort probabilities list by probability in desc order (list of tuples e.g. (cluster_no, probabilty))\n",
    "  # Second: get cluster number of first element (highest probability) \n",
    "  # Third: convert cluster number (lda_dict key) to predefined cluser name (lda_dict value)\n",
    "\n",
    "  texts_clean = texts_clean.apply( lambda t: topics_dict[sorted(t, reverse=True, key=cmp_to_key(lambda item1, item2: item1[1] - item2[1]))[0][0]] )\n",
    "\n",
    "  return texts_clean"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Classify the users descriptions into labels resulted from lda base model\n",
    "topic_df['urs_industry_categ']= classifier(topic_df['usr_description_tokens'],lda_model12,lda_dict)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Tuning Users' Description LDA "
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The base LDA model had a **coherence score of 0.5167870900**. \n",
    "While this is not an inherently bad score, this section of the notebook tries to provide an LDA model which records a higher coherence score. \n",
    "To increase the coherence of a LDA model multiple techniques can be employed such as tweaking the parameters of the model or assuring further data cleaning for words considered as noise. \n",
    "\n",
    "In this project the following techniques were used:\n",
    "* further cleaning for noise words (Section: Data Preprocessing)\n",
    "* tweaking the *k - num_topics (number of topics)* parameter of the LDA model \n",
    "\n",
    "As a result it can be seen that a LDA with coherence above **0.56** was returned. This model was further used to label the topics of the user descriptions so that each user could be categorized to one industry. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "#The code in this cell is inspired by Sahil Mahajan's (2021) article:  https://www.linkedin.com/pulse/nlp-a-complete-guide-topic-modeling-latent-dirichlet-sahil-m/\n",
    "\n",
    "#Define function to compute coherence for LDA models with different choices of topic numbers \n",
    "def compute_coherence_values(corpus_, dictionary_,text,limit = 1,start=6,step=2):\n",
    "\n",
    "    coherence_vals=[]\n",
    "    model_list=[]\n",
    "\n",
    "    for num_topics in range(start,limit,step):\n",
    "        #Train LDA model with num_topics number of topics\n",
    "        model= LdaMulticore(corpus_, id2word=dictionary_, num_topics=num_topics, workers = 4, passes=10)\n",
    "        #Calculate coherence of the model trained\n",
    "        coherence_model = CoherenceModel(model=model, texts=text, dictionary=dictionary_, coherence='c_v')\n",
    "        #Append coherence values and model list to the coherence_vals and model_lists respectively\n",
    "        coherence_vals.append(coherence_model.get_coherence())\n",
    "        model_list.append(model)\n",
    "\n",
    "    return model_list, coherence_vals\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Compute coherence scores for LDA models with number of topics between 6 and 20 with steps of 2\n",
    "user_model_list, coherence_values = compute_coherence_values(corpus, dictionary,topic_df['usr_description_tokens'],limit=20,start=6,step=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<function matplotlib.pyplot.show(close=None, block=None)>"
      ]
     },
     "execution_count": 150,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#The code in this cell is inspired by Sahil Mahajan's (2021) article:  https://www.linkedin.com/pulse/nlp-a-complete-guide-topic-modeling-latent-dirichlet-sahil-m/\n",
    "#Plot coherence values resulted from computing multiple coherences\n",
    "\n",
    "graph_coherence= range(6, 20, 2)\n",
    "plt.plot(graph_coherence, coherence_values)\n",
    "plt.title(\"Coherence Scores for LDA by No. of Topics\")\n",
    "plt.xlabel(\"No. of Topics\")\n",
    "plt.ylabel(\"Coherence Score\")\n",
    "plt.savefig('usr_topicmodel_coherenceV1.png')\n",
    "plt.show"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{6: (0.5149889959374491,\n",
       "  <gensim.models.ldamulticore.LdaMulticore at 0x26f80cd2770>),\n",
       " 8: (0.5651888382512682,\n",
       "  <gensim.models.ldamulticore.LdaMulticore at 0x2703dbc8af0>),\n",
       " 10: (0.5275255483817933,\n",
       "  <gensim.models.ldamulticore.LdaMulticore at 0x2703dbca860>),\n",
       " 12: (0.5354888633754634,\n",
       "  <gensim.models.ldamulticore.LdaMulticore at 0x27051a54eb0>),\n",
       " 14: (0.5194994557703561,\n",
       "  <gensim.models.ldamulticore.LdaMulticore at 0x26fb0956e60>),\n",
       " 16: (0.505631277196121,\n",
       "  <gensim.models.ldamulticore.LdaMulticore at 0x26f80cd1b40>),\n",
       " 18: (0.5424035303121868,\n",
       "  <gensim.models.ldamulticore.LdaMulticore at 0x27037683940>)}"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Create dictionary of resulting models and coherence scores, that will be used to select best performing LDA model\n",
    "res_models = {graph_coherence[i]: (coherence_values[i],user_model_list[i]) for i in range(len(graph_coherence))}\n",
    "res_models"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<gensim.models.ldamulticore.LdaMulticore at 0x2703dbc8af0>"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#store best performing LDA model from the previous step as usr_ldaModel\n",
    "usr_ldaModel = res_models[8][1]\n",
    "usr_ldaModel"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "<link rel=\"stylesheet\" type=\"text/css\" href=\"https://cdn.jsdelivr.net/gh/bmabey/pyLDAvis@3.3.1/pyLDAvis/js/ldavis.v1.0.0.css\">\n",
       "\n",
       "\n",
       "<div id=\"ldavis_el182026818953683205373425017\"></div>\n",
       "<script type=\"text/javascript\">\n",
       "\n",
       "var ldavis_el182026818953683205373425017_data = {\"mdsDat\": {\"x\": [-0.048654203347696726, 0.3067920478002715, 0.2710169352515637, -0.18299843541659871, -0.10738273865065115, 0.04025699247288849, -0.12643439940547938, -0.15259619870429753], \"y\": [0.1910577329541089, -0.03670989670199575, 0.15377384910046618, 0.0372473258699815, 0.05649711924278689, -0.34003450391049217, -0.15215842172175384, 0.09032679516689864], \"topics\": [1, 2, 3, 4, 5, 6, 7, 8], \"cluster\": [1, 1, 1, 1, 1, 1, 1, 1], \"Freq\": [16.532619026081445, 14.111549665298655, 13.206657938581671, 12.623616872075127, 12.340408293302653, 11.564677729928995, 10.433019055724104, 9.18745141900735]}, \"tinfo\": {\"Term\": [\"engineer\", \"founder\", \"software\", \"developer\", \"business\", \"life\", \"crypto\", \"digital\", \"enthusiast\", \"co\", \"make\", \"world\", \"music\", \"artist\", \"marketing\", \"nft\", \"art\", \"build\", \"good\", \"designer\", \"director\", \"web\", \"view\", \"investor\", \"phd\", \"news\", \"help\", \"writer\", \"student\", \"learn\", \"business\", \"marketing\", \"news\", \"service\", \"brand\", \"company\", \"solution\", \"growth\", \"late\", \"strategy\", \"platform\", \"website\", \"provide\", \"marketer\", \"digital\", \"sale\", \"industry\", \"seo\", \"tool\", \"tip\", \"agency\", \"update\", \"trend\", \"client\", \"saa\", \"chatgpt\", \"customer\", \"scale\", \"firm\", \"deliver\", \"online\", \"insight\", \"build\", \"content\", \"medium\", \"help\", \"product\", \"management\", \"lead\", \"technology\", \"ai\", \"social\", \"expert\", \"tech\", \"creator\", \"community\", \"design\", \"web\", \"follow\", \"founder\", \"tweet\", \"know\", \"say\", \"truth\", \"back\", \"may\", \"girl\", \"still\", \"pronoun\", \"little\", \"kid\", \"shall\", \"hate\", \"veteran\", \"die\", \"night\", \"shit\", \"boy\", \"block\", \"one\", \"lose\", \"fight\", \"really\", \"ask\", \"stan\", \"joke\", \"question\", \"marry\", \"else\", \"even\", \"mus\", \"account\", 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\"tweet\", \"tech\"], \"Freq\": [19125.0, 21806.0, 16207.0, 15865.0, 17039.0, 16269.0, 11452.0, 14925.0, 16414.0, 11042.0, 16744.0, 14581.0, 9656.0, 9600.0, 11110.0, 8345.0, 9178.0, 14278.0, 15117.0, 9008.0, 8194.0, 16349.0, 11201.0, 7215.0, 8142.0, 9373.0, 13760.0, 13634.0, 10911.0, 12038.0, 17038.251550718738, 11109.386427621801, 9372.938581464046, 5457.1282528356805, 5401.514624874319, 4700.424750738658, 4466.464205392224, 4072.954832242649, 4059.3021138144645, 3943.858718031851, 3640.244749563423, 3268.182145887243, 3247.3022445734973, 2975.7538609026064, 14921.392229559086, 2893.3213450502426, 2826.350583017294, 2735.796560943028, 2683.9727632719023, 2678.2951229960427, 2653.8502851360527, 2318.824406543559, 1834.9625816053695, 1744.1033465928285, 1709.688270902531, 1696.3250104937424, 1619.0222653362357, 1464.518718613966, 1176.62800472634, 1163.8218042678993, 5128.550152024227, 3361.1834320273106, 11955.284703889844, 7773.425076119156, 6925.90929484625, 9252.67232698473, 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13681.103074922283, 7612.073825062181, 4749.418185365481, 10305.165466448288, 3303.702771693724, 2657.857669574842, 2999.5099050382737, 3271.3765236363165, 2745.334674341263, 6127.213337671757, 6833.844253099784, 2176.9868386938188, 2684.435592322704, 3497.419903343248, 2789.024068939782, 2687.305559840091, 6011.967978231088, 3162.2333616823153, 3865.6779468795166, 6266.216500445954, 3634.943663257021, 4281.603115574301, 5147.485888073317, 4506.99254739464, 2745.4222786807877, 2697.145377964622, 2667.808288680913, 19124.521137671833, 16206.998026231264, 15864.968709457109, 9007.449136598227, 6136.905922746877, 5135.651248611553, 4778.155858005345, 4435.44579621717, 3169.9073204224487, 3015.33362122534, 5892.59064149875, 2624.2937298606835, 2578.881008249689, 2460.354479457169, 2574.361342548494, 2062.135629498709, 1670.2630874412275, 1488.1614374068838, 1467.2170881241634, 1463.4345423705824, 1394.2390289521907, 1392.2679112426745, 1349.7538990832734, 1335.4899794313862, 1290.9425776692947, 1213.1073108297153, 1130.7837501139115, 1090.1454428580073, 983.3185998132104, 974.6354447695076, 6099.964065634672, 1408.2662446465001, 12045.841049176852, 2541.7127078895774, 9274.07285181933, 4230.622689160801, 5550.1895803926145, 7413.19950930365, 7860.628251086447, 4432.493070615815, 5204.083341642599, 3083.9758818958508, 1898.7686200529258, 1842.0076714852305, 3630.830664510206, 3103.1244890469916, 2360.6706235527317, 2984.1309210556633, 2893.2684449275057, 2968.748819928043, 2603.889777953958, 8141.993424857671, 5404.122381945955, 5387.633141756775, 4570.947431223017, 3682.044797792201, 3588.2373696982013, 3425.3941899789934, 2725.8240515303205, 2714.9267644082342, 2407.9771717164567, 2224.9821159366543, 2217.129723468436, 2190.588987417329, 2068.149403771231, 1866.719534258743, 1742.7496633449136, 1668.009317150272, 1561.901137238179, 1557.1059758012602, 1513.8383801788605, 1466.3278777727805, 1281.7363564014556, 1235.3149140067515, 1164.500533459725, 1143.9650767662865, 1088.9627038754277, 1026.7443572399156, 1014.8945173109282, 980.3629216019284, 909.4554417579311, 4043.6362413913976, 1952.6230338031053, 1643.3571997786428, 6773.020317803283, 4817.8091811910135, 2893.684332186966, 7926.413499844846, 7224.584766687, 3773.0519835025148, 4181.560630458613, 3023.363178813616, 3660.1317020900888, 3231.9936880689033, 3245.5044425144993, 5857.609461017021, 3972.4786328479845, 2828.967004422238, 4124.635742673651, 2773.5713662657854, 3370.469640029875, 2246.0192135478887, 2443.9998549892002, 9655.639688303445, 9599.346963831005, 9177.906122132195, 3703.8090481786376, 2725.0662581173497, 2242.495913808849, 1978.343172910129, 1878.718033376847, 1874.5820402773072, 1588.1903410796017, 1551.172770655534, 1526.3364994485064, 1289.1916422256656, 1185.005349382638, 1166.1792336655533, 1110.4622852439868, 1082.0976704983343, 1074.8439078878143, 1074.4397085621774, 1026.390986427459, 947.2565161559721, 945.1997563690641, 924.5315678632109, 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1284.520671723282, 1208.694363934295, 1208.6101588326283, 1157.077666644393, 1099.764035004213, 1099.5348529232238, 1019.4873030447687, 1004.4281519321095, 991.3100957930405, 975.3973058056861, 871.3588555189174, 3702.582954569221, 3137.374964793873, 1799.2221668437514, 1474.6321863908538, 4228.26312972892, 2432.569851916487, 3114.542249336415, 4367.80806455184, 1545.248936554602, 1879.4084276745955, 2863.141523529379, 1724.3328446712726, 1539.1909760497767, 1457.2255069137864], \"Total\": [19125.0, 21806.0, 16207.0, 15865.0, 17039.0, 16269.0, 11452.0, 14925.0, 16414.0, 11042.0, 16744.0, 14581.0, 9656.0, 9600.0, 11110.0, 8345.0, 9178.0, 14278.0, 15117.0, 9008.0, 8194.0, 16349.0, 11201.0, 7215.0, 8142.0, 9373.0, 13760.0, 13634.0, 10911.0, 12038.0, 17039.151142128194, 11110.282327334442, 9373.83457562641, 5458.024263430156, 5402.410496507795, 4701.320730562392, 4467.439096981712, 4073.851080619261, 4060.198092755535, 3944.7547254946708, 3641.140751555076, 3269.078119589509, 3248.198062743559, 2976.6498454366524, 14925.88600460807, 2894.217334684976, 2827.248372767673, 2736.692327411274, 2684.868741065121, 2679.191185698936, 2654.7460575460514, 2319.720338463926, 1835.8585543456713, 1744.9991853823105, 1710.5841410078492, 1697.2211569319202, 1619.9180692082286, 1465.4145804432226, 1177.5240658154196, 1164.7175999913168, 5138.137453314772, 3392.424031154112, 14278.585439606355, 9775.095660733474, 9255.13170516821, 13760.434653451264, 8974.241216730166, 4018.367346297258, 7088.974236702846, 11382.493048239972, 14403.4393485763, 11067.394326366219, 5513.894163861313, 18827.984344576733, 9026.76940042315, 6977.410412164636, 8370.307483941086, 16349.596088102511, 10050.475231822847, 21806.716723042013, 19826.15392515227, 5649.157615199295, 3326.712212218117, 3013.361746064207, 3002.029957655037, 2865.9001288512222, 2520.374901556061, 2324.13054035181, 2273.53815113723, 2049.511939897732, 2027.7222363737028, 1968.9282639710211, 1934.9086720175699, 1892.398567321831, 1881.848743423578, 1800.2356364836348, 1779.0860776991537, 1726.2934644760999, 1713.1117646624512, 1706.8412964430531, 1649.941889936684, 1496.184871573604, 1439.950534559547, 1437.861506999186, 1419.919982860816, 1384.777978888031, 1310.1918089359924, 1270.3641552440286, 1269.311273356898, 1191.8452308573796, 1132.8718496834247, 7831.588435555535, 7941.6134039929, 2576.1107760010714, 3490.27061151869, 6746.409601215893, 5049.118011983323, 6128.067564927311, 4206.934585953574, 4909.999665227861, 10050.475231822847, 3753.610152309672, 8420.796429234068, 17667.073805509983, 4590.926208733558, 15117.553331028661, 12457.880304136153, 11913.958007463205, 7472.504818149501, 9721.306123522421, 19826.15392515227, 7028.064303612864, 2422.6444895409377, 2201.901097549423, 1939.7808642770501, 1835.402204736721, 1613.8021092539843, 1533.8559061850012, 1470.6618221729586, 1385.1105108388113, 1362.2279272845783, 1347.126635812331, 1342.6612238117461, 1300.775724717756, 1292.0296757028932, 1253.0823260651696, 1111.3603854834823, 1009.4639588229586, 974.0241720966354, 966.4858350874184, 959.8735994473565, 879.7560015497526, 876.2015934073115, 837.5865537700537, 826.2956896337738, 760.2349996822479, 759.4261770493315, 735.957878998665, 719.0167456636035, 708.8592151919861, 669.6747650221952, 668.7177197802025, 4743.605910995785, 741.0315229816146, 16269.139754188329, 4949.529664035643, 2578.8551081660344, 14581.469945178147, 16744.62236625038, 9301.284538243613, 5823.6047602846165, 15117.553331028661, 4145.64206436468, 3303.384597033986, 3840.2792728764593, 4306.927060982305, 3555.5082184252383, 9721.306123522421, 12038.692171280687, 2674.277495625614, 3585.028562433676, 5245.079954560946, 3854.7812156007603, 3653.948357660495, 12457.880304136153, 4779.8676931589725, 6667.792678507528, 14581.662739389478, 6467.678870501111, 10615.04418969857, 17667.073805509983, 13760.434653451264, 6213.094544257314, 6788.711411409936, 8420.796429234068, 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0.22640926431511404, 0.7720415286273143, 0.1438099737026118, 0.8556849411415709, 0.9995258036172544, 0.9989905716025745, 0.998585922136396, 0.13688425220590247, 0.4297177977566057, 0.2036118961920463, 0.14195911927165236, 0.001577323547462804, 0.08627274011774815, 0.16616980380645685, 0.0010972830626922452, 0.8327006842005775, 0.2245386343868531, 0.20392254780883667, 0.06055239143485965, 0.00023561241803447334, 0.35636378227714094, 0.15444394002159728, 0.15666177781677862, 0.3420008754492691, 0.5012296767789631, 0.35102670399024877, 0.6485205039577054, 0.9991882402223837], \"Term\": [\"academic\", \"account\", \"account\", \"account\", \"account\", \"account\", \"achieve\", \"activist\", \"actor\", \"advice\", \"advisor\", \"advocate\", \"advocate\", \"advocate\", \"advocate\", \"agency\", \"ai\", \"ai\", \"ai\", \"ai\", \"also\", \"also\", \"also\", \"also\", \"also\", \"also\", \"also\", \"alum\", \"analyst\", \"analyst\", \"analyst\", \"angel\", \"anime\", \"architect\", \"art\", \"artificial\", \"artist\", \"ask\", \"assistant\", \"attorney\", \"author\", \"author\", \"author\", \"author\", \"award\", \"back\", \"bad\", \"bad\", \"banner\", \"become\", \"beer\", \"bio\", \"bitcoin\", \"block\", \"blockchain\", \"blockchain\", \"board\", \"book\", \"book\", \"boy\", \"brand\", \"btc\", \"build\", \"build\", \"business\", \"buy\", \"buy\", \"buy\", \"candidate\", \"capital\", \"cat\", \"cat\", \"cat\", \"ceo\", \"ceo\", \"ceo\", \"challenge\", \"change\", \"change\", \"chatgpt\", \"chief\", \"chief\", \"client\", \"climate\", \"cloud\", \"cloud\", \"co\", \"coach\", \"coach\", \"coach\", \"code\", \"code\", \"coffee\", \"coffee\", \"collector\", \"collector\", \"college\", \"college\", \"columnist\", \"come\", \"come\", \"comic\", \"comm\", \"community\", \"community\", \"community\", \"community\", \"community\", \"community\", \"company\", \"computer\", \"content\", \"content\", \"content\", \"cook\", \"copy\", \"copywriter\", \"create\", \"create\", \"creator\", \"creator\", \"creator\", \"creator\", \"creator\", \"critic\", \"critical\", \"crypto\", \"cryptocurrency\", \"customer\", \"cyber\", \"dark\", \"data\", \"data\", \"data\", \"datum\", \"day\", \"day\", \"day\", \"dc\", \"dedicate\", \"defi\", \"degen\", \"deliver\", \"design\", \"design\", \"designer\", \"dev\", \"developer\", \"devop\", \"die\", \"different\", \"digital\", \"digital\", \"director\", \"dog\", \"dog\", \"dog\", \"draw\", \"dream\", \"dream\", \"easy\", \"economic\", \"economics\", \"ed\", \"edit\", \"editor\", \"education\", \"education\", \"education\", \"education\", \"else\", \"employer\", \"endorsement\", \"energy\", \"engineer\", \"engineering\", \"english\", \"english\", \"english\", \"enjoyer\", \"enjoyer\", \"enthusiast\", \"enthusiast\", \"entrepreneur\", \"entrepreneur\", \"entrepreneur\", \"entrepreneur\", \"estate\", \"even\", \"everyday\", \"ex\", \"ex\", \"ex\", \"expert\", \"expert\", \"expert\", \"explorer\", \"express\", \"fan\", \"fan\", \"fan\", \"fan\", \"fan\", \"fantasy\", \"fc\", \"fi\", \"fiction\", \"fight\", \"finance\", \"finance\", \"finance\", \"financial\", \"find\", \"find\", \"find\", \"find\", \"firm\", \"follow\", \"follow\", \"follow\", \"follow\", \"food\", \"food\", \"football\", \"former\", \"former\", \"former\", \"former\", \"formerly\", \"formerly\", \"founder\", \"founder\", \"founder\", \"free\", \"free\", \"free\", \"free\", \"front\", \"frontend\", \"full\", \"full\", \"fund\", \"future\", \"future\", \"future\", \"future\", \"game\", \"game\", \"gamer\", \"gamer\", \"get\", \"get\", \"get\", \"girl\", \"go\", \"go\", \"go\", \"goal\", \"good\", \"good\", \"good\", \"good\", \"graphic\", \"great\", \"great\", \"great\", \"growth\", \"hacker\", \"hate\", \"head\", \"head\", \"head\", \"head\", \"head\", \"head\", \"health\", \"health\", \"healthy\", \"help\", \"help\", \"history\", \"history\", \"hockey\", \"holder\", \"host\", \"human\", \"human\", \"human\", \"human\", \"idea\", \"idea\", \"ig\", \"ig\", \"important\", \"industry\", \"influencer\", \"inquiry\", \"insight\", \"insight\", \"inspire\", \"instagram\", \"intelligence\", \"intelligence\", \"interest\", \"interest\", \"interested\", \"interested\", \"intern\", \"invest\", \"investment\", \"investor\", \"io\", \"javascript\", \"joke\", \"journalist\", \"justice\", \"kid\", \"know\", \"late\", \"law\", \"lead\", \"lead\", \"lead\", \"learn\", \"learn\", \"learner\", \"learner\", \"let\", \"let\", \"liberal\", \"life\", \"life\", \"lifelong\", \"like\", \"like\", \"like\", \"like\", \"like\", \"little\", \"live\", \"live\", \"live\", \"live\", \"look\", \"look\", \"lose\", \"love\", \"love\", \"love\", \"love\", \"lover\", \"lover\", \"lover\", \"machine\", \"make\", \"make\", \"make\", \"management\", \"management\", \"management\", \"market\", \"market\", \"marketer\", \"marketing\", \"marry\", \"may\", \"medical\", \"medicine\", \"medium\", \"medium\", \"medium\", \"member\", \"member\", \"member\", \"member\", \"mental\", \"metaverse\", \"microsoft\", \"microsoft\", \"mind\", \"mind\", \"mind\", \"mindset\", \"mine\", \"minor\", \"ml\", \"money\", \"money\", \"month\", \"movie\", \"mus\", \"music\", \"nerd\", \"nerd\", \"nerd\", \"nerd\", \"new\", \"new\", \"new\", \"new\", \"new\", \"new\", \"new\", \"news\", \"nft\", \"night\", \"novel\", \"nyc\", \"occasionally\", \"offer\", \"officer\", \"old\", \"old\", \"one\", \"online\", \"online\", \"open\", \"open\", \"open\", \"opinion\", \"opinion\", \"opinion\", \"opinion\", \"opinion\", \"option\", \"pa\", \"people\", \"people\", \"personal\", \"personal\", \"personal\", \"personal\", \"personal\", \"pfp\", \"pfp\", \"phd\", \"philosophy\", \"photographer\", \"photographer\", \"photographer\", \"place\", \"place\", \"plant\", \"platform\", \"play\", \"play\", \"player\", \"player\", \"player\", \"podcast\", \"policy\", \"political\", \"politics\", \"politics\", \"pop\", \"positive\", \"president\", \"press\", \"problem\", \"producer\", \"producer\", \"product\", \"product\", \"prof\", \"professor\", \"programmer\", \"programming\", \"progress\", \"promote\", \"pronoun\", \"proud\", \"proud\", \"proud\", \"proud\", \"provide\", \"psychology\", \"psychology\", \"public\", \"public\", \"public\", \"public\", \"put\", \"python\", \"question\", \"react\", \"read\", \"reader\", \"real\", \"real\", \"real\", \"really\", \"reflect\", \"relationship\", \"reporter\", \"research\", \"research\", \"researcher\", \"researcher\", \"retweet\", \"review\", \"right\", \"right\", \"rock\", \"saa\", \"sale\", \"say\", \"scale\", \"school\", \"school\", \"science\", \"science\", \"scientist\", \"scientist\", \"second\", \"security\", \"see\", \"see\", \"self\", \"self\", \"senior\", \"senior\", \"senior\", \"seo\", \"service\", \"set\", \"shall\", \"share\", \"share\", \"shit\", \"simple\", \"social\", \"social\", \"social\", \"software\", \"solution\", \"solve\", \"speaker\", \"speech\", \"sport\", \"sport\", \"sport\", \"sport\", \"stack\", \"stan\", \"step\", \"still\", \"stock\", \"storyteller\", \"strategy\", \"streamer\", \"student\", \"student\", \"study\", \"success\", \"system\", \"system\", \"system\", \"system\", \"take\", \"take\", \"teacher\", \"teacher\", \"teacher\", \"teacher\", \"teacher\", \"tech\", \"tech\", \"tech\", \"tech\", \"tech\", \"tech\", \"tech\", \"technology\", \"technology\", \"technology\", \"technology\", \"technology\", \"theory\", \"think\", \"think\", \"time\", \"time\", \"time\", \"time\", \"time\", \"time\", \"time\", \"tip\", \"together\", \"tool\", \"trade\", \"trader\", \"trading\", \"travel\", \"travel\", \"travel\", \"trend\", \"true\", \"truth\", \"try\", \"try\", \"tweet\", \"tweet\", \"tweet\", \"tweet\", \"tweet\", \"tweet\", \"tweet\", \"tweet\", \"twitch\", \"twitter\", \"twitter\", \"twitter\", \"understand\", \"university\", \"update\", \"use\", \"use\", \"use\", \"use\", \"use\", \"ux\", \"vc\", \"veteran\", \"vice\", \"video\", \"video\", \"video\", \"view\", \"view\", \"way\", \"way\", \"web\", \"web\", \"web\", \"website\", \"welcome\", \"well\", \"well\", \"well\", \"win\", \"win\", \"wine\", \"winner\", \"wisdom\", \"work\", \"work\", \"work\", \"work\", \"work\", \"work\", \"world\", \"world\", \"world\", \"write\", \"write\", \"write\", \"write\", \"write\", \"write\", \"writer\", \"writer\", \"writer\", \"youtube\", \"youtube\", \"youtubecom\"]}, \"R\": 30, \"lambda.step\": 0.01, \"plot.opts\": {\"xlab\": \"PC1\", \"ylab\": \"PC2\"}, \"topic.order\": [2, 8, 3, 1, 6, 7, 4, 5]};\n",
       "\n",
       "function LDAvis_load_lib(url, callback){\n",
       "  var s = document.createElement('script');\n",
       "  s.src = url;\n",
       "  s.async = true;\n",
       "  s.onreadystatechange = s.onload = callback;\n",
       "  s.onerror = function(){console.warn(\"failed to load library \" + url);};\n",
       "  document.getElementsByTagName(\"head\")[0].appendChild(s);\n",
       "}\n",
       "\n",
       "if(typeof(LDAvis) !== \"undefined\"){\n",
       "   // already loaded: just create the visualization\n",
       "   !function(LDAvis){\n",
       "       new LDAvis(\"#\" + \"ldavis_el182026818953683205373425017\", ldavis_el182026818953683205373425017_data);\n",
       "   }(LDAvis);\n",
       "}else if(typeof define === \"function\" && define.amd){\n",
       "   // require.js is available: use it to load d3/LDAvis\n",
       "   require.config({paths: {d3: \"https://d3js.org/d3.v5\"}});\n",
       "   require([\"d3\"], function(d3){\n",
       "      window.d3 = d3;\n",
       "      LDAvis_load_lib(\"https://cdn.jsdelivr.net/gh/bmabey/pyLDAvis@3.3.1/pyLDAvis/js/ldavis.v3.0.0.js\", function(){\n",
       "        new LDAvis(\"#\" + \"ldavis_el182026818953683205373425017\", ldavis_el182026818953683205373425017_data);\n",
       "      });\n",
       "    });\n",
       "}else{\n",
       "    // require.js not available: dynamically load d3 & LDAvis\n",
       "    LDAvis_load_lib(\"https://d3js.org/d3.v5.js\", function(){\n",
       "         LDAvis_load_lib(\"https://cdn.jsdelivr.net/gh/bmabey/pyLDAvis@3.3.1/pyLDAvis/js/ldavis.v3.0.0.js\", function(){\n",
       "                 new LDAvis(\"#\" + \"ldavis_el182026818953683205373425017\", ldavis_el182026818953683205373425017_data);\n",
       "            })\n",
       "         });\n",
       "}\n",
       "</script>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Visualized LDA of 8 topics with best coherence\n",
    "usr_lda_display8 = pyLDAvis.gensim_models.prepare(usr_ldaModel, corpus, dictionary)\n",
    "pyLDAvis.display(usr_lda_display8)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Save LDA Model on user description with 8 resulting topics\n",
    "usr_ldaModel.save('post_TunedUserLDA/userLDA_Tuned')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[(0,\n",
       "  '0.038*\"engineer\" + 0.032*\"software\" + 0.031*\"developer\" + 0.024*\"enthusiast\" + 0.018*\"web\" + 0.018*\"designer\" + 0.016*\"tech\" + 0.015*\"ai\" + 0.012*\"dev\" + 0.012*\"full\"'),\n",
       " (1,\n",
       "  '0.026*\"business\" + 0.022*\"digital\" + 0.018*\"build\" + 0.017*\"marketing\" + 0.014*\"news\" + 0.014*\"help\" + 0.012*\"content\" + 0.010*\"medium\" + 0.009*\"ai\" + 0.009*\"product\"'),\n",
       " (2,\n",
       "  '0.028*\"life\" + 0.026*\"make\" + 0.023*\"world\" + 0.019*\"good\" + 0.014*\"people\" + 0.013*\"learn\" + 0.012*\"work\" + 0.012*\"live\" + 0.011*\"time\" + 0.010*\"love\"'),\n",
       " (3,\n",
       "  '0.042*\"founder\" + 0.026*\"co\" + 0.020*\"director\" + 0.018*\"author\" + 0.017*\"view\" + 0.016*\"writer\" + 0.016*\"ceo\" + 0.014*\"editor\" + 0.014*\"opinion\" + 0.013*\"host\"'),\n",
       " (4,\n",
       "  '0.031*\"crypto\" + 0.023*\"nft\" + 0.020*\"investor\" + 0.015*\"financial\" + 0.012*\"advice\" + 0.012*\"trader\" + 0.012*\"enthusiast\" + 0.011*\"entrepreneur\" + 0.010*\"market\" + 0.010*\"speaker\"'),\n",
       " (5,\n",
       "  '0.016*\"phd\" + 0.016*\"student\" + 0.015*\"science\" + 0.014*\"research\" + 0.012*\"social\" + 0.011*\"university\" + 0.011*\"professor\" + 0.010*\"health\" + 0.009*\"endorsement\" + 0.008*\"politics\"'),\n",
       " (6,\n",
       "  '0.021*\"music\" + 0.021*\"artist\" + 0.020*\"art\" + 0.016*\"lover\" + 0.016*\"game\" + 0.015*\"fan\" + 0.014*\"love\" + 0.010*\"writer\" + 0.010*\"sport\" + 0.009*\"book\"'),\n",
       " (7,\n",
       "  '0.012*\"account\" + 0.010*\"twitter\" + 0.010*\"know\" + 0.008*\"follow\" + 0.008*\"go\" + 0.008*\"love\" + 0.007*\"day\" + 0.007*\"good\" + 0.007*\"get\" + 0.006*\"take\"')]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Test reading the model from stored location\n",
    "usr_ldaModel = models.ldamodel.LdaModel.load('post_TunedUserLDA/userLDA_Tuned')\n",
    "usr_ldaModel.print_topics()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Create dictionary that stores labels for each of the resulting topics from usr_ldaModel\n",
    "usr_lda_dict = {0:'Software Engineering',\n",
    "            1:'Marketing & Business Development',\n",
    "            2:'NotIndustry(1)',\n",
    "            3:'Entrepreneurship & Business Leadership',\n",
    "            4:'Finance & Crypto',\n",
    "            5:'Education & Academia',\n",
    "            6:'Arts & Entertainment',\n",
    "            7:'NotIndustry(2)',\n",
    "            '-':'-'}\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x600 with 8 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Code in this cell was inspired by Selva Prabhakaran (source url: https://www.machinelearningplus.com/nlp/topic-modeling-visualization-how-to-present-results-lda-models/ )\n",
    "\n",
    "#Visualize all 8 topics identified\n",
    "\n",
    "cols = [color for color in ['#ff8625','#6f93ff','#f56cff','#224f3c','#ea9999','#981c41','#05504c','#6a2292']] #mcolors.TABLEAU_COLORS.items()]  # more colors: 'mcolors.XKCD_COLORS'\n",
    "\n",
    "x, y = np.ogrid[:300, :300]\n",
    "mask = (x - 150) ** 2 + (y - 150) ** 2 > 130 ** 2\n",
    "mask = 255 * mask.astype(int)\n",
    "\n",
    "cloud = WordCloud(stopwords=extra_stop_words,\n",
    "                  background_color='white',\n",
    "                  width=2500,\n",
    "                  height=1800,\n",
    "                  max_words=10,\n",
    "                  colormap='hsv_r',\n",
    "                  mask=mask,\n",
    "                  color_func=lambda *args, **kwargs: cols[i],\n",
    "                  prefer_horizontal=1.0)\n",
    "\n",
    "topics = usr_ldaModel.show_topics(formatted=False)\n",
    "\n",
    "fig, axes = plt.subplots(4, 2, figsize=(6,6), sharex=True, sharey=True)\n",
    "\n",
    "for i, ax in enumerate(axes.flatten()):\n",
    "    fig.add_subplot(ax)\n",
    "    topic_words = dict(topics[i][1])\n",
    "    cloud.generate_from_frequencies(topic_words, max_font_size=300)\n",
    "    plt.gca().imshow(cloud)\n",
    "    plt.gca().set_title(usr_lda_dict[i], fontdict=dict(size=10))\n",
    "    plt.gca().axis('off')\n",
    "\n",
    "\n",
    "plt.subplots_adjust(wspace=0, hspace=0)\n",
    "plt.axis('off')\n",
    "plt.margins(x=0, y=0)\n",
    "plt.tight_layout()\n",
    "plt.savefig(\"UserDescritption_TopicsVisual.jpg\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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+/PkHbgcAgIeF/E3+BgDYJ3I4ORwAYH/I3+Rv4GGjQAAoxD766CN5e3vr5s2b5mUXL16Ui4uLnnzySYt1t2/fLpPJpMOHDz/UGCdMmCCTySSTySQnJyf5+/vrpZde0h9//PFQ43gQKSkpatOmTZbPNWnSRCkpKfL19ZWU/aBrz549GjBgQEGGCQCwE+Tvh4P8DQDIb+Twh4McDgDIT+Tvh4P8DdgWCgSAQqx58+a6ePGi4uPjzcu2b9+usmXLas+ePbp8+bJ5eWxsrMqXL6+goKA8t3Pr1i2lp6ffd5w1atRQSkqKTp48qfnz5+ubb75Rnz59CqStglC2bFm5ubll+Zyrq6vKli0rk8mU4z5KlSolDw+PgggPAGBnyN8PB/kbAJDfyOEPBzkcAJCfyN8PB/kbsC0UCACFWLVq1VS+fHnFxsaal8XGxioyMlJVqlTRrl27LJY3b95ckvTXX3+pT58+Kl68uDw8PNSmTRsdOXLEvG5GBd+6desUEhIiNzc3nThxQmfOnFGHDh3k7u6uSpUqacmSJbmK09nZWWXLllWFChXUvn17DR48WN99952uXLmSbVv3ijHDmjVrFBQUpKJFi+qpp57SqVOnzM8dO3ZMkZGRKlOmjLy8vPTYY4/p+++/z7SPCxcuqGfPnvLy8lL58uX1/vvvWzx/5/RId7tzeqTY2Fi9+OKLSk1NNVd8TpgwQVLm6ZFSU1M1YMAAlS5dWj4+PmrRooX27dtnfn7fvn1q3ry5vL295ePjowYNGlgMYgEA9ov8Tf4GANgncjg5HABgf8jf5G/AEVEgABRy4eHh2rp1q/nx1q1bFR4errCwMPPy69ev68cffzQPbqKiohQfH6+1a9fqxx9/lGEYatu2rW7cuGHez+XLlzV16lR9+umnOnDggEqXLq2oqCglJydry5YtWrFihebNm6czZ87kOWZ3d3elp6ebp3XKrq3cxPj2229r8eLF2rlzp9LS0tS9e3fz8xcvXlTbtm31/fffa+/evYqIiFCHDh108uRJi3jeeecd1a5dWz///LPeeOMNDRs2TJs3b85zv5o0aaI5c+bIx8dHKSkpSklJ0YgRIzKtZxiG2rVrp9OnT2vDhg1KSEhQ/fr11bJlS/3555+SpF69eumRRx7Rnj17lJCQoNGjR8vFxSXPMQEAbBP5m/wNALBP5HByOADA/pC/yd+AwzEAFGqffPKJ4enpady4ccNIS0sznJ2djf/973/GsmXLjCZNmhiGYRjbtm0zJBnHjh0zDh8+bEgydu7cad7H2bNnDXd3d2P58uWGYRhGdHS0IclITEw0r3Po0CFDkrF7927zsqSkJEOSMXv27GzjGz9+vFGnTh2LbQIDA41GjRpl21ZeYswqnri4uGzjCQkJMd5//33z44oVKxqtW7e2WKdbt25GmzZtzI8lGatXrzYMwzCOHz9uSDL27t1rGIZhbN261ZBk/PXXX+a4fH19M7VbsWJF8+sUExNj+Pj4GFevXrVYp0qVKsbHH39sGIZheHt7G4sWLcq2HwAA+0b+Jn8DAOwTOZwcDgCwP+Rv8jfgaJhBACjkmjdvrkuXLmnPnj3avn27goKCVLp0aYWFhWnPnj26dOmSYmNj9eijj6py5cpKSkqSs7OzGjdubN6Hn5+fqlWrpqSkJPMyV1dX1a5d2/w4Y7uGDRual1WvXl3FihW7Z4z79++Xl5eX3N3dFRISIn9/f4uplbJr614xZhdPxjqXLl3SqFGjFBISomLFisnLy0u//vprpurH0NDQTI/vbCe/JSQk6OLFi/Lz85OXl5f55/jx4zp27Jgk6bXXXtNLL72kVq1aadq0aeblAIDCgfxN/gYA2CdyODkcAGB/yN/kb8DROFs7AAAFKzAwUI888oi2bt2qv/76S2FhYZKksmXLqlKlStq5c6e2bt2qFi1aSLo9NU9WDMOQyWQyP3Z3d7d4nLHdnctyq1q1alq7dq2cnJxUvnx5ubm5WTyfXVv3ijG7eDKWjRw5Ups2bdLMmTMVGBgod3d3Pfvss7p+/fo9Y76ffuZWenq6ypUrZ3HfqwwZg8UJEyaoZ8+eWr9+vb799luNHz9ey5YtU6dOnQosLgDAw0P+Jn8DAOwTOZwcDgCwP+Rv8jfgaJhBAHAAzZs3V2xsrGJjYxUeHm5eHhYWpk2bNmn37t3meyeFhITo5s2biouLM6937tw5HT58WMHBwdm2ERwcrJs3byo+Pt687NChQzp//vw943N1dVVgYKAqVaqUaWCTldzGmF081atXlyRt375dUVFR6tSpk2rVqqWyZcsqOTk5U3u7d+/O9DhjH3nl6uqqW7du5bhO/fr1dfr0aTk7OyswMNDip2TJkub1goKCNGzYMH333Xfq3LmzoqOj7ysmAIBtIn9bxkP+BgDYC3K4ZTzkcACAPSB/W8ZD/gYKNwoEAAfQvHlz7dixQ4mJiebqR+n24GbBggW6evWqeXBTtWpVRUZGqn///tqxY4f27dun3r17q0KFCoqMjMy2jWrVqql169bq37+/4uLilJCQoJdeeknu7u753p/cxuji4qJXX31VcXFx+vnnn/Xiiy/q8ccfV6NGjSTdrgxdtWqVEhMTtW/fPvXs2VPp6emZ2tu5c6dmzJihw4cP68MPP9TXX3+tIUOG3FfsAQEBunjxomJiYnT27Fldvnw50zqtWrVSaGioOnbsqE2bNik5OVm7du3SmDFjFB8frytXrmjQoEGKjY3ViRMntHPnTu3ZsyfHwScAwP6Qv8nfAAD7RA4nhwMA7A/5m/wNOBIKBAAH0Lx5c125ckWBgYEqU6aMeXlYWJguXLigKlWqyN/f37w8OjpaDRo0UPv27RUaGirDMLRhwwa5uLjk2E50dLT8/f0VFhamzp07a8CAASpdunSB9Ck3MXp4eOj1119Xz549FRoaKnd3dy1btsz8/OzZs1W8eHE1adJEHTp0UEREhOrXr5+preHDhyshIUH16tXT5MmTNWvWLEVERNxX3E2aNNHAgQPVrVs3lSpVSjNmzMi0jslk0oYNG9SsWTP17dtXQUFB6t69u5KTk1WmTBk5OTnp3Llz6tOnj4KCgvTcc8+pTZs2mjhx4n3FBACwTeRv8jcAwD6Rw8nhAAD7Q/4mfwOOxGRkdyMSAAAAAAAAAAAAAABQaDCDAAAAAAAAAAAAAAAADoACAQAAAAAAAAAAAAAAHAAFAgAAAAAAAAAAAAAAOAAKBAAAAAAAAAAAAAAAcAAUCAAAAAAAAAAAAAAA4AAoEAAAAAAAAAAAAAAAwAFQIAAAAAAAAAAAAAAAgAOgQAAAAAAAAAAAAAAAAAdAgQAAAAAAAAAAAAAAAA6AAgEAAAAAAAAAAAAAABwABQIAAAAAAAAAAAAAADgACgQAAAAAAAAAAAAAAHAA/w+NLEyhEvLIJgAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 2000x1000 with 8 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axes = plt.subplots(nrows=2, ncols=4, figsize=(20,10))\n",
    "axes = axes.ravel()\n",
    "\n",
    "colors = ['#118DFF', '#009292', '#F64F5C', '#750985','#480091','#F2C80F','#73B761','#074650']\n",
    "\n",
    "# plot the top words for each topic as a horizontal bar plot\n",
    "for topic_num in range(usr_ldaModel.num_topics):\n",
    "    words = [word for word, prob in usr_ldaModel.show_topic(topic_num)]\n",
    "    probs = [prob for word, prob in usr_ldaModel.show_topic(topic_num)]\n",
    "    probs_sorted, words_sorted = zip(*sorted(zip(probs, words), reverse=False))\n",
    "    axes[topic_num].barh(words_sorted, probs_sorted,color=colors[topic_num])\n",
    "    axes[topic_num].set_yticklabels(words_sorted, ha='right')\n",
    "    axes[topic_num].set_xlabel('Word Probabilities')\n",
    "    axes[topic_num].set_ylabel('Top Words')\n",
    "    axes[topic_num].set_title(f'T{topic_num+1}: {usr_lda_dict[topic_num]}', fontsize=16)\n",
    "    fig.subplots_adjust(wspace=1.1, hspace=1)\n",
    "    plt.savefig(f'user_topic_probabilities.jpg')\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()\n",
    "#plt.savefig(f'user_topic_probabilities.jpg', bbox_inches='tight')"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Based on the results from the LDA model resulting in the above 8 topics, each tweeter user is clasisifed into an industry. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Classify the user descriptions into the industries identified based on usr_ldaModel\n",
    "topic_df['urs_industry_categ']= classifier(topic_df['usr_description_tokens'],usr_ldaModel,usr_lda_dict)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Arts & Entertainment', 'NotIndustry(1)',\n",
       "       'Entreprenourship & Business Commentary', 'Software Engingeering',\n",
       "       'NotIndustry(2)', 'Marketing and Business Development',\n",
       "       'Education & Academia', 'Finance & Crypto'], dtype=object)"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Check labelling \n",
    "topic_df['urs_industry_categ'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "metadata": {},
   "outputs": [],
   "source": [
    "#TODO:only remove last comment below if labels need to be modified\n",
    "#Merge the topic_df with the rows that are classified as NotSpecified and save the new data\n",
    "#pd.concat([topic_df,user_prepro_df[user_prepro_df[\"urs_industry_categ\"]==\"NotSpecified\"]]).to_csv(\"usr_TopicLabelledFINAL.csv\",index= False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(698722, 10)"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "user_data = pd.read_csv(\"data_files//usr_TopicLabelledFINAL.csv\")\n",
    "user_data['usr_userid']=user_data['usr_userid'].astype(str)\n",
    "user_data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "urs_industry_categ\n",
       "Arts & Entertainment                       68138\n",
       "Education & Academia                       65601\n",
       "Entreprenourship & Business Commentary     52426\n",
       "Finance & Crypto                           47440\n",
       "Marketing and Business Development         93379\n",
       "NotIndustry(1)                             76223\n",
       "NotIndustry(2)                            101387\n",
       "NotSpecified                              105090\n",
       "Software Engingeering                      89038\n",
       "Name: usr_userid, dtype: int64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Check count of all categories\n",
    "user_data.groupby([\"urs_industry_categ\"])['usr_userid'].count()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "CodeBase",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.4"
  },
  "orig_nbformat": 4,
  "vscode": {
   "interpreter": {
    "hash": "2b626e21ead191b3db0e87a4603b3e94e198395ed9547c98ae8f08e151842829"
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}