4_Notebook_Tweets_SA_and_TopicModelling.ipynb

{
 "cells": [
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Sentiment Analysis & Tweet Topic Modelling"
   ]
  },
  {
   "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",
    "In answering the research question a focal point consists of determining the sentiments of the tweets about ChatGPT. To do so, this notebook showcases how a pretrained lexicon-based model, Vader, was employed to get the polarity of each tweet. Based on these polarities the tweets were then assigned either a Positive, Neutral or Negative label. \n",
    "\n",
    "To gain more insights related to the the tweets on ChatGPT, the second part of this notebook centers around finding the topics that were touched upon in these tweets. \n",
    "For topic modelling two methods were tried: Latent Dirichlet Allocation and key-work analysis. The results of these analysis techniques were not fully conclusive due to data considerations."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Import cell of necessary packages\n",
    "import pandas as pd\n",
    "from pandas import option_context\n",
    "import numpy as np\n",
    "import datetime\n",
    "import ast #for reading data as specific data type\n",
    "import re, string #for regex data cleaning \n",
    "from string import digits #for handling digits \n",
    "import itertools\n",
    "import ast\n",
    "\n",
    "#Tweet cleaning package\n",
    "import preprocessor as p\n",
    "p.set_options(p.OPT.URL, p.OPT.MENTION)\n",
    "\n",
    "#Set pandas options for ease of cleaning\n",
    "pd.set_option('display.max_rows', 500)\n",
    "pd.options.mode.chained_assignment = None\n",
    "\n",
    "#Import to surpass long future warnings\n",
    "import warnings\n",
    "warnings.simplefilter(action='ignore')\n",
    "warnings.filterwarnings(\"ignore\", category=DeprecationWarning) \n",
    "\n",
    "#Import package for comparing value to key dict\n",
    "from functools import cmp_to_key \n",
    "\n",
    "#Import coherence to find most optimal number of topics\n",
    "from gensim.models import CoherenceModel\n",
    "\n",
    "#Import for visualisations\n",
    "from matplotlib import pyplot as plt\n",
    "#Package for wordclouds\n",
    "from wordcloud import WordCloud, STOPWORDS\n",
    "\n",
    "#Import Vader for Sentiment Analysis\n",
    "from vaderSentiment.vaderSentiment import SentimentIntensityAnalyzer\n",
    "\n",
    "#Import LDA model packages\n",
    "from gensim.corpora.dictionary import Dictionary # Import the dictionary builder\n",
    "from gensim.models import LdaMulticore #we'll use the faster multicore version of LDA\n",
    "\n",
    "#Import to visualize LDA models\n",
    "import pyLDAvis\n",
    "#Import to save LDA models\n",
    "import pyLDAvis.gensim_models as gensimvis\n",
    "from gensim import  models\n",
    "\n",
    "#Import module for VADER performance assesment\n",
    "from sklearn.metrics import classification_report"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Tweet Sentiment Analysis"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This section showcases how the the sentiment of tweets is deducted by using a pre-trained lexicon based mode: Vader. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>tweet_id</th>\n",
       "      <th>tweet_content</th>\n",
       "      <th>tweet_content_hashtags</th>\n",
       "      <th>tweet_content_mentions</th>\n",
       "      <th>tweet_content_URLs</th>\n",
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       "      <th>tweet_content_tokens_no_verbs</th>\n",
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       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>1644446066748854281</td>\n",
       "      <td>What's happening, it asks, and I reply that ch...</td>\n",
       "      <td>['teaching']</td>\n",
       "      <td>[]</td>\n",
       "      <td>[]</td>\n",
       "      <td>NaN</td>\n",
       "      <td>whats happening it asks and i reply that chat ...</td>\n",
       "      <td>['class', 'kind', 'world', 'english', 'alterna...</td>\n",
       "      <td>['class', 'kind', 'world', 'english', 'alterna...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1644442136664764444</td>\n",
       "      <td>...one in the depot is approximately 17,42 MB ...</td>\n",
       "      <td>[]</td>\n",
       "      <td>[]</td>\n",
       "      <td>[]</td>\n",
       "      <td>NaN</td>\n",
       "      <td>one in the depot is approximately mb in size t...</td>\n",
       "      <td>['correct', 'size', 'chatgpt', 'depot', 'curre...</td>\n",
       "      <td>['correct', 'size', 'chatgpt', 'depot', 'curre...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>1644437753721942044</td>\n",
       "      <td>I’ve converted to chatgpt</td>\n",
       "      <td>[]</td>\n",
       "      <td>[]</td>\n",
       "      <td>[]</td>\n",
       "      <td>NaN</td>\n",
       "      <td>ive converted to chatgpt</td>\n",
       "      <td>['chatgpt']</td>\n",
       "      <td>['chatgpt']</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "   Unnamed: 0             tweet_id  \\\n",
       "0           0  1644446066748854281   \n",
       "1           1  1644442136664764444   \n",
       "2           2  1644437753721942044   \n",
       "\n",
       "                                       tweet_content tweet_content_hashtags  \\\n",
       "0  What's happening, it asks, and I reply that ch...           ['teaching']   \n",
       "1  ...one in the depot is approximately 17,42 MB ...                     []   \n",
       "2                          I’ve converted to chatgpt                     []   \n",
       "\n",
       "  tweet_content_mentions tweet_content_URLs tweet_content_emoji  \\\n",
       "0                     []                 []                 NaN   \n",
       "1                     []                 []                 NaN   \n",
       "2                     []                 []                 NaN   \n",
       "\n",
       "                                 tweet_content_clean  \\\n",
       "0  whats happening it asks and i reply that chat ...   \n",
       "1  one in the depot is approximately mb in size t...   \n",
       "2                           ive converted to chatgpt   \n",
       "\n",
       "                                tweet_content_tokens  \\\n",
       "0  ['class', 'kind', 'world', 'english', 'alterna...   \n",
       "1  ['correct', 'size', 'chatgpt', 'depot', 'curre...   \n",
       "2                                        ['chatgpt']   \n",
       "\n",
       "                       tweet_content_tokens_no_verbs  \n",
       "0  ['class', 'kind', 'world', 'english', 'alterna...  \n",
       "1  ['correct', 'size', 'chatgpt', 'depot', 'curre...  \n",
       "2                                        ['chatgpt']  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Read tokanized tweets data\n",
    "sentiment_df = pd.read_csv('data_files/Tweets_PreLDA.csv')\n",
    "sentiment_df['tweet_id']=sentiment_df['tweet_id'].astype(str)\n",
    "sentiment_df.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>tweet_id</th>\n",
       "      <th>tweet_content</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1644446066748854281</td>\n",
       "      <td>What's happening, it asks, and I reply that chat GPT has my comp class in the fall as a kind of video game.  Create an alternative you and then you and I work together to save the world from English.  #teaching</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1644442136664764444</td>\n",
       "      <td>...one in the depot is approximately 17,42 MB in size! The current american.gxt is 4,97 MB in size, and in the depot it is approximately 5,22 MB in size! If the conversions are correct (The conversions where done from MiB to MB via ChatGPT), then that means that they...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1644437753721942044</td>\n",
       "      <td>I’ve converted to chatgpt</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1644433541827280896</td>\n",
       "      <td>Asked #chatgpt if someone has a BMI of 29, are they obese, and it gave me a reply about a bmi of 29 having health risks, however, it did not account for the fact that body fat %age also factors in to the health of a person. Goes to show that docs can’t be replaced by AI yet</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1644429028307406849</td>\n",
       "      <td>How does ChatGPT train its AI using advanced hardware? Find out in my new blog post where I explore the secrets of ChatGPT’s AI training. \\nhttps://t.co/Wo2IQqh1WG\\n#ChatGPT #AI #Hardware #blog</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1644424509217206277</td>\n",
       "      <td>I CAN DO SCRIPTS ASWELL!!</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1644421142399053843</td>\n",
       "      <td>@enokay69 This one dier chatgpt oo 😂</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>1644417202928459782</td>\n",
       "      <td>@Ajax777 Why? They can become 10X writers with ChatGPT, just like I did.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>1644414016062910465</td>\n",
       "      <td>ChatGPT is the engineering friend group I never got to make because I didn't get to finish my degree at GaTech because of $$$</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>1644409427544481827</td>\n",
       "      <td>@Aslan_Bey_1 ChatGPT doesnt even say he was the Messiah, it says he claimed he was. Two completely different things 😂😂\\n\\nSo they're excited because they lack reading skills.</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              tweet_id  \\\n",
       "0  1644446066748854281   \n",
       "1  1644442136664764444   \n",
       "2  1644437753721942044   \n",
       "3  1644433541827280896   \n",
       "4  1644429028307406849   \n",
       "5  1644424509217206277   \n",
       "6  1644421142399053843   \n",
       "7  1644417202928459782   \n",
       "8  1644414016062910465   \n",
       "9  1644409427544481827   \n",
       "\n",
       "                                                                                                                                                                                                                                                                        tweet_content  \n",
       "0                                                                  What's happening, it asks, and I reply that chat GPT has my comp class in the fall as a kind of video game.  Create an alternative you and then you and I work together to save the world from English.  #teaching  \n",
       "1      ...one in the depot is approximately 17,42 MB in size! The current american.gxt is 4,97 MB in size, and in the depot it is approximately 5,22 MB in size! If the conversions are correct (The conversions where done from MiB to MB via ChatGPT), then that means that they...  \n",
       "2                                                                                                                                                                                                                                                           I’ve converted to chatgpt  \n",
       "3  Asked #chatgpt if someone has a BMI of 29, are they obese, and it gave me a reply about a bmi of 29 having health risks, however, it did not account for the fact that body fat %age also factors in to the health of a person. Goes to show that docs can’t be replaced by AI yet  \n",
       "4                                                                                   How does ChatGPT train its AI using advanced hardware? Find out in my new blog post where I explore the secrets of ChatGPT’s AI training. \\nhttps://t.co/Wo2IQqh1WG\\n#ChatGPT #AI #Hardware #blog  \n",
       "5                                                                                                                                                                                                                                                           I CAN DO SCRIPTS ASWELL!!  \n",
       "6                                                                                                                                                                                                                                                @enokay69 This one dier chatgpt oo 😂  \n",
       "7                                                                                                                                                                                                            @Ajax777 Why? They can become 10X writers with ChatGPT, just like I did.  \n",
       "8                                                                                                                                                       ChatGPT is the engineering friend group I never got to make because I didn't get to finish my degree at GaTech because of $$$  \n",
       "9                                                                                                      @Aslan_Bey_1 ChatGPT doesnt even say he was the Messiah, it says he claimed he was. Two completely different things 😂😂\\n\\nSo they're excited because they lack reading skills.  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Inspect tweet_content column\n",
    "with option_context('display.max_colwidth', 400):\n",
    "    display(sentiment_df[['tweet_id','tweet_content']].head(10))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Create Sentiment Analisys Analyzer object\n",
    "analyzer = SentimentIntensityAnalyzer()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Before fitting the *analyzer* object to the tweets data, some pre-cleaning on the content of the tweets was done. This consisted in removal of urls and mentions, and in getting the content of hashtags as independent words. No further cleaning was needed as according to [*Vader's documentation*](https://github.com/cjhutto/vaderSentiment#features-and-updates) Vader performs well with emojis, punctuation and letter-capitalisation."
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Code inspo for splitting hashtags: \n",
    "https://stackoverflow.com/questions/68448243/efficient-way-to-split-multi-word-hashtag-in-python"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Naive function of hashtag removal\n",
    "#Function below was inspired by the answers in this stackoverflow thread: https://stackoverflow.com/questions/68448243/efficient-way-to-split-multi-word-hashtag-in-python\n",
    "def do_process_eng_hashtag(input_text: str) -> str:\n",
    "    return re.sub(\n",
    "        r'#[a-zA-Z0-9]\\S*',\n",
    "        lambda m: \n",
    "        ' '.join(re.findall('[A-Z][^A-Z]*|[a-z][^A-Z]*', m.group().lstrip('#'))) \n",
    "        if len(m.group().lstrip('#')) >3 else m.group().lstrip('#'),\n",
    "        input_text,\n",
    "    )\n",
    "#Function to remove mentions and usl\n",
    "def prepro_sentiment(dataframe, dataframe_column):\n",
    "    #Remove urls and mentions\n",
    "    dataframe[f'{dataframe_column}_sentiment_prepro'] = dataframe[f'{dataframe_column}'].apply(lambda x: p.clean(str(x)))\n",
    "    print('cleaning done')\n",
    "    #Split hashtags\n",
    "    dataframe[f'{dataframe_column}_sentiment_prepro'] = dataframe[f'{dataframe_column}_sentiment_prepro'] .apply(lambda x: do_process_eng_hashtag(str(x)))\n",
    "    #remove digits \n",
    "    print('hashtags done')\n",
    "    dataframe[f'{dataframe_column}_sentiment_prepro'] = dataframe[f'{dataframe_column}_sentiment_prepro'].apply(lambda x: ''.join([i for i in x if not i.isdigit()]))\n",
    "\n",
    "    return dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Create dataframe of only columns needed for sentiment analysis \n",
    "sentiment_processed_df = sentiment_df[['tweet_id','tweet_content']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "cleaning done\n",
      "hashtags done\n"
     ]
    }
   ],
   "source": [
    "#Pre-process tweet_content\n",
    "sentiment_processed_df = prepro_sentiment(sentiment_processed_df, 'tweet_content')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
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       "      <td>1644446066748854281</td>\n",
       "      <td>What's happening, it asks, and I reply that chat GPT has my comp class in the fall as a kind of video game.  Create an alternative you and then you and I work together to save the world from English.  #teaching</td>\n",
       "      <td>What's happening, it asks, and I reply that chat GPT has my comp class in the fall as a kind of video game. Create an alternative you and then you and I work together to save the world from English. teaching</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1644442136664764444</td>\n",
       "      <td>...one in the depot is approximately 17,42 MB in size! The current american.gxt is 4,97 MB in size, and in the depot it is approximately 5,22 MB in size! If the conversions are correct (The conversions where done from MiB to MB via ChatGPT), then that means that they...</td>\n",
       "      <td>...one in the depot is approximately , MB in size! The current american.gxt is , MB in size, and in the depot it is approximately , MB in size! If the conversions are correct (The conversions where done from MiB to MB via ChatGPT), then that means that they...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1644437753721942044</td>\n",
       "      <td>I’ve converted to chatgpt</td>\n",
       "      <td>I’ve converted to chatgpt</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1644433541827280896</td>\n",
       "      <td>Asked #chatgpt if someone has a BMI of 29, are they obese, and it gave me a reply about a bmi of 29 having health risks, however, it did not account for the fact that body fat %age also factors in to the health of a person. Goes to show that docs can’t be replaced by AI yet</td>\n",
       "      <td>Asked chatgpt if someone has a BMI of , are they obese, and it gave me a reply about a bmi of  having health risks, however, it did not account for the fact that body fat %age also factors in to the health of a person. Goes to show that docs can’t be replaced by AI yet</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1644429028307406849</td>\n",
       "      <td>How does ChatGPT train its AI using advanced hardware? Find out in my new blog post where I explore the secrets of ChatGPT’s AI training. \\nhttps://t.co/Wo2IQqh1WG\\n#ChatGPT #AI #Hardware #blog</td>\n",
       "      <td>How does ChatGPT train its AI using advanced hardware? Find out in my new blog post where I explore the secrets of ChatGPT’s AI training. Chat G P T AI Hardware blog</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1644424509217206277</td>\n",
       "      <td>I CAN DO SCRIPTS ASWELL!!</td>\n",
       "      <td>I CAN DO SCRIPTS ASWELL!!</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1644421142399053843</td>\n",
       "      <td>@enokay69 This one dier chatgpt oo 😂</td>\n",
       "      <td>This one dier chatgpt oo 😂</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>1644417202928459782</td>\n",
       "      <td>@Ajax777 Why? They can become 10X writers with ChatGPT, just like I did.</td>\n",
       "      <td>Why? They can become X writers with ChatGPT, just like I did.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>1644414016062910465</td>\n",
       "      <td>ChatGPT is the engineering friend group I never got to make because I didn't get to finish my degree at GaTech because of $$$</td>\n",
       "      <td>ChatGPT is the engineering friend group I never got to make because I didn't get to finish my degree at GaTech because of $$$</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>1644409427544481827</td>\n",
       "      <td>@Aslan_Bey_1 ChatGPT doesnt even say he was the Messiah, it says he claimed he was. Two completely different things 😂😂\\n\\nSo they're excited because they lack reading skills.</td>\n",
       "      <td>ChatGPT doesnt even say he was the Messiah, it says he claimed he was. Two completely different things 😂😂 So they're excited because they lack reading skills.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>1644405181767983113</td>\n",
       "      <td>While generative AI models like Bard or chatgpt goes more in the action step direction. They both have a role to play but with different purposes. And different parts of the making ideas happen-process. And I think we've been often lazy and using search wrong.</td>\n",
       "      <td>While generative AI models like Bard or chatgpt goes more in the action step direction. They both have a role to play but with different purposes. And different parts of the making ideas happen-process. And I think we've been often lazy and using search wrong.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>1644401228040871937</td>\n",
       "      <td>6/ In summary, FactSet's partnership with AI language models like ChatGPT has the potential to enhance natural language processing capabilities for financial data analysis and provide reliable data sources for financial institutions.  Definitely a stock to keep your eye on</td>\n",
       "      <td>/ In summary, FactSet's partnership with AI language models like ChatGPT has the potential to enhance natural language processing capabilities for financial data analysis and provide reliable data sources for financial institutions. Definitely a stock to keep your eye on</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>1644397697825161218</td>\n",
       "      <td>@EmmGeee chatGPT</td>\n",
       "      <td>chatGPT</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>1644394074617544719</td>\n",
       "      <td>The newest version of ChatGPT passed the US medical licensing exam with flying colors — and diagnosed a 1 in 100,000 condition in seconds https://t.co/8P6bfzujBS</td>\n",
       "      <td>The newest version of ChatGPT passed the US medical licensing exam with flying colors — and diagnosed a  in , condition in seconds</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>1644390876347604993</td>\n",
       "      <td>Somebody was bitching about the book cuz it was mid journey and thought to be chat gpt lol I said omg he even typed using his fingers gross</td>\n",
       "      <td>Somebody was bitching about the book cuz it was mid journey and thought to be chat gpt lol I said omg he even typed using his fingers gross</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               tweet_id  \\\n",
       "0   1644446066748854281   \n",
       "1   1644442136664764444   \n",
       "2   1644437753721942044   \n",
       "3   1644433541827280896   \n",
       "4   1644429028307406849   \n",
       "5   1644424509217206277   \n",
       "6   1644421142399053843   \n",
       "7   1644417202928459782   \n",
       "8   1644414016062910465   \n",
       "9   1644409427544481827   \n",
       "10  1644405181767983113   \n",
       "11  1644401228040871937   \n",
       "12  1644397697825161218   \n",
       "13  1644394074617544719   \n",
       "14  1644390876347604993   \n",
       "\n",
       "                                                                                                                                                                                                                                                                         tweet_content  \\\n",
       "0                                                                   What's happening, it asks, and I reply that chat GPT has my comp class in the fall as a kind of video game.  Create an alternative you and then you and I work together to save the world from English.  #teaching   \n",
       "1       ...one in the depot is approximately 17,42 MB in size! The current american.gxt is 4,97 MB in size, and in the depot it is approximately 5,22 MB in size! If the conversions are correct (The conversions where done from MiB to MB via ChatGPT), then that means that they...   \n",
       "2                                                                                                                                                                                                                                                            I’ve converted to chatgpt   \n",
       "3   Asked #chatgpt if someone has a BMI of 29, are they obese, and it gave me a reply about a bmi of 29 having health risks, however, it did not account for the fact that body fat %age also factors in to the health of a person. Goes to show that docs can’t be replaced by AI yet   \n",
       "4                                                                                    How does ChatGPT train its AI using advanced hardware? Find out in my new blog post where I explore the secrets of ChatGPT’s AI training. \\nhttps://t.co/Wo2IQqh1WG\\n#ChatGPT #AI #Hardware #blog   \n",
       "5                                                                                                                                                                                                                                                            I CAN DO SCRIPTS ASWELL!!   \n",
       "6                                                                                                                                                                                                                                                 @enokay69 This one dier chatgpt oo 😂   \n",
       "7                                                                                                                                                                                                             @Ajax777 Why? They can become 10X writers with ChatGPT, just like I did.   \n",
       "8                                                                                                                                                        ChatGPT is the engineering friend group I never got to make because I didn't get to finish my degree at GaTech because of $$$   \n",
       "9                                                                                                       @Aslan_Bey_1 ChatGPT doesnt even say he was the Messiah, it says he claimed he was. Two completely different things 😂😂\\n\\nSo they're excited because they lack reading skills.   \n",
       "10                While generative AI models like Bard or chatgpt goes more in the action step direction. They both have a role to play but with different purposes. And different parts of the making ideas happen-process. And I think we've been often lazy and using search wrong.   \n",
       "11   6/ In summary, FactSet's partnership with AI language models like ChatGPT has the potential to enhance natural language processing capabilities for financial data analysis and provide reliable data sources for financial institutions.  Definitely a stock to keep your eye on   \n",
       "12                                                                                                                                                                                                                                                                    @EmmGeee chatGPT   \n",
       "13                                                                                                                   The newest version of ChatGPT passed the US medical licensing exam with flying colors — and diagnosed a 1 in 100,000 condition in seconds https://t.co/8P6bfzujBS   \n",
       "14                                                                                                                                         Somebody was bitching about the book cuz it was mid journey and thought to be chat gpt lol I said omg he even typed using his fingers gross   \n",
       "\n",
       "                                                                                                                                                                                                                                                     tweet_content_sentiment_prepro  \n",
       "0                                                                   What's happening, it asks, and I reply that chat GPT has my comp class in the fall as a kind of video game. Create an alternative you and then you and I work together to save the world from English. teaching  \n",
       "1              ...one in the depot is approximately , MB in size! The current american.gxt is , MB in size, and in the depot it is approximately , MB in size! If the conversions are correct (The conversions where done from MiB to MB via ChatGPT), then that means that they...  \n",
       "2                                                                                                                                                                                                                                                         I’ve converted to chatgpt  \n",
       "3     Asked chatgpt if someone has a BMI of , are they obese, and it gave me a reply about a bmi of  having health risks, however, it did not account for the fact that body fat %age also factors in to the health of a person. Goes to show that docs can’t be replaced by AI yet  \n",
       "4                                                                                                             How does ChatGPT train its AI using advanced hardware? Find out in my new blog post where I explore the secrets of ChatGPT’s AI training. Chat G P T AI Hardware blog  \n",
       "5                                                                                                                                                                                                                                                         I CAN DO SCRIPTS ASWELL!!  \n",
       "6                                                                                                                                                                                                                                                        This one dier chatgpt oo 😂  \n",
       "7                                                                                                                                                                                                                     Why? They can become X writers with ChatGPT, just like I did.  \n",
       "8                                                                                                                                                     ChatGPT is the engineering friend group I never got to make because I didn't get to finish my degree at GaTech because of $$$  \n",
       "9                                                                                                                    ChatGPT doesnt even say he was the Messiah, it says he claimed he was. Two completely different things 😂😂 So they're excited because they lack reading skills.  \n",
       "10             While generative AI models like Bard or chatgpt goes more in the action step direction. They both have a role to play but with different purposes. And different parts of the making ideas happen-process. And I think we've been often lazy and using search wrong.  \n",
       "11  / In summary, FactSet's partnership with AI language models like ChatGPT has the potential to enhance natural language processing capabilities for financial data analysis and provide reliable data sources for financial institutions. Definitely a stock to keep your eye on  \n",
       "12                                                                                                                                                                                                                                                                          chatGPT  \n",
       "13                                                                                                                                               The newest version of ChatGPT passed the US medical licensing exam with flying colors — and diagnosed a  in , condition in seconds  \n",
       "14                                                                                                                                      Somebody was bitching about the book cuz it was mid journey and thought to be chat gpt lol I said omg he even typed using his fingers gross  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Inspect results of preprocessing\n",
    "with option_context('display.max_colwidth', 400):\n",
    "    display(sentiment_processed_df[['tweet_id','tweet_content','tweet_content_sentiment_prepro']].head(15))"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To asses how VADER performs on the given dataset, a small sampled was hand-labelled by the authors of this study. The results of VADER were then compared to the manually  labelled data to calculate the recall, precision and F1 score of VADER's performance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Get 250 rows sample\n",
    "n_test_rows= 250\n",
    "test_set= sentiment_processed_df.sample(n_test_rows).reset_index()\n",
    "test_set.shape\n",
    "#TODO:remove comment to save sample of data\n",
    "#test_set.to_csv('test_set_unlabelled.csv',index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Small script used in manually labelling the data\n",
    "for row in range(test_set.shape[0]):\n",
    "    #Slice dataframe so row by row can be appended in resulting csv file\n",
    "    df_to_append = test_set[test_set.index==row]\n",
    "    #Display tweet and request input value\n",
    "    input_val = input(f\"ROW:{row} \\nTweet { test_set['tweet_content_sentiment_prepro'][row]}\\n Insert Rating: \")\n",
    "    #Assign input value to df_to_append column\n",
    "    df_to_append[row,'sentiment_hand_labelled']= input_val\n",
    "    df_to_append.to_csv('hand_labelled_data.csv',index=False,mode='a',header=False)   \n",
    "\n",
    "    if input_val=='x':\n",
    "        break\n",
    "   \n",
    "#Values: 0 - Neutral, 1 - Negative, 2- Positive "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>index</th>\n",
       "      <th>tweet_id</th>\n",
       "      <th>tweet_content</th>\n",
       "      <th>tweet_content_sentiment_prepro</th>\n",
       "      <th>sentimen_label_manual</th>\n",
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       "      <td>1613078056104493056</td>\n",
       "      <td>If you are looking to get professional access ...</td>\n",
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       "      <th>1</th>\n",
       "      <td>1</td>\n",
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       "      <td>I used ChatGPT to write a contract for me and ...</td>\n",
       "      <td>I used ChatGPT to write a contract for me and ...</td>\n",
       "      <td>2</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>1599824753417560064</td>\n",
       "      <td>what happens when these chatGPT systems fail i...</td>\n",
       "      <td>what happens when these chatGPT systems fail i...</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>1625356830812536839</td>\n",
       "      <td>@iexistentiai I cannot stop reading the chatgp...</td>\n",
       "      <td>I cannot stop reading the chatgpt and bing red...</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>1643078207770615808</td>\n",
       "      <td>@google used #ChatGPT data sets to improve #BA...</td>\n",
       "      <td>used Chat G P T data sets to improve B A R D. ...</td>\n",
       "      <td>2</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   index             tweet_id  \\\n",
       "0      0  1613078056104493056   \n",
       "1      1  1631035154633457664   \n",
       "2      2  1599824753417560064   \n",
       "3      3  1625356830812536839   \n",
       "4      4  1643078207770615808   \n",
       "\n",
       "                                       tweet_content  \\\n",
       "0  If you are looking to get professional access ...   \n",
       "1  I used ChatGPT to write a contract for me and ...   \n",
       "2  what happens when these chatGPT systems fail i...   \n",
       "3  @iexistentiai I cannot stop reading the chatgp...   \n",
       "4  @google used #ChatGPT data sets to improve #BA...   \n",
       "\n",
       "                      tweet_content_sentiment_prepro  sentimen_label_manual  \n",
       "0  If you are looking to get professional access ...                      2  \n",
       "1  I used ChatGPT to write a contract for me and ...                      2  \n",
       "2  what happens when these chatGPT systems fail i...                      1  \n",
       "3  I cannot stop reading the chatgpt and bing red...                      2  \n",
       "4  used Chat G P T data sets to improve B A R D. ...                      2  "
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#read manually labelled data\n",
    "results= pd.read_csv('data_files/hand_labelled_data.csv',names=['idx','tweet_id','tweet_content','tweet_content_sentiment_prepro','sentimen_label_manual']).drop(columns='idx').reset_index()\n",
    "results.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Get polarity scores per tweet\n",
    "results['vader_polarity'] = results['tweet_content_sentiment_prepro'].apply(lambda x: analyzer.polarity_scores(x))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>index</th>\n",
       "      <th>tweet_id</th>\n",
       "      <th>tweet_content</th>\n",
       "      <th>tweet_content_sentiment_prepro</th>\n",
       "      <th>sentimen_label_manual</th>\n",
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       "      <td>If you are looking to get professional access ...</td>\n",
       "      <td>2</td>\n",
       "      <td>{'neg': 0.0, 'neu': 1.0, 'pos': 0.0, 'compound...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1631035154633457664</td>\n",
       "      <td>I used ChatGPT to write a contract for me and ...</td>\n",
       "      <td>I used ChatGPT to write a contract for me and ...</td>\n",
       "      <td>2</td>\n",
       "      <td>{'neg': 0.0, 'neu': 0.852, 'pos': 0.148, 'comp...</td>\n",
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       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>1599824753417560064</td>\n",
       "      <td>what happens when these chatGPT systems fail i...</td>\n",
       "      <td>what happens when these chatGPT systems fail i...</td>\n",
       "      <td>1</td>\n",
       "      <td>{'neg': 0.216, 'neu': 0.651, 'pos': 0.134, 'co...</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>1625356830812536839</td>\n",
       "      <td>@iexistentiai I cannot stop reading the chatgp...</td>\n",
       "      <td>I cannot stop reading the chatgpt and bing red...</td>\n",
       "      <td>2</td>\n",
       "      <td>{'neg': 0.173, 'neu': 0.706, 'pos': 0.121, 'co...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>1643078207770615808</td>\n",
       "      <td>@google used #ChatGPT data sets to improve #BA...</td>\n",
       "      <td>used Chat G P T data sets to improve B A R D. ...</td>\n",
       "      <td>2</td>\n",
       "      <td>{'neg': 0.0, 'neu': 0.952, 'pos': 0.048, 'comp...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   index             tweet_id  \\\n",
       "0      0  1613078056104493056   \n",
       "1      1  1631035154633457664   \n",
       "2      2  1599824753417560064   \n",
       "3      3  1625356830812536839   \n",
       "4      4  1643078207770615808   \n",
       "\n",
       "                                       tweet_content  \\\n",
       "0  If you are looking to get professional access ...   \n",
       "1  I used ChatGPT to write a contract for me and ...   \n",
       "2  what happens when these chatGPT systems fail i...   \n",
       "3  @iexistentiai I cannot stop reading the chatgp...   \n",
       "4  @google used #ChatGPT data sets to improve #BA...   \n",
       "\n",
       "                      tweet_content_sentiment_prepro  sentimen_label_manual  \\\n",
       "0  If you are looking to get professional access ...                      2   \n",
       "1  I used ChatGPT to write a contract for me and ...                      2   \n",
       "2  what happens when these chatGPT systems fail i...                      1   \n",
       "3  I cannot stop reading the chatgpt and bing red...                      2   \n",
       "4  used Chat G P T data sets to improve B A R D. ...                      2   \n",
       "\n",
       "                                      vader_polarity  \n",
       "0  {'neg': 0.0, 'neu': 1.0, 'pos': 0.0, 'compound...  \n",
       "1  {'neg': 0.0, 'neu': 0.852, 'pos': 0.148, 'comp...  \n",
       "2  {'neg': 0.216, 'neu': 0.651, 'pos': 0.134, 'co...  \n",
       "3  {'neg': 0.173, 'neu': 0.706, 'pos': 0.121, 'co...  \n",
       "4  {'neg': 0.0, 'neu': 0.952, 'pos': 0.048, 'comp...  "
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Inspect dataframe\n",
    "results.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Function to assign sentiment per tweet based on compound polarity\n",
    "def polarity_assignement(dataframe_input): \n",
    "    for i in range(dataframe_input.shape[0]):\n",
    "        compound = dataframe_input.at[i,'vader_polarity']['compound']\n",
    "        \n",
    "        if compound > 0.05:\n",
    "            dataframe_input.at[i,'sentiment'] = \"Positive\"\n",
    "        elif compound < -0.05:\n",
    "            dataframe_input.at[i,'sentiment'] = \"Negative\"\n",
    "        else:\n",
    "            dataframe_input.at[i,'sentiment'] = \"Neutral\"\n",
    "    return dataframe_input"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Dict to map values in sentiment_label_manual column\n",
    "manual_vals = {0:'Neutral',1:'Negative',2:'Positive'}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>index</th>\n",
       "      <th>tweet_id</th>\n",
       "      <th>tweet_content</th>\n",
       "      <th>tweet_content_sentiment_prepro</th>\n",
       "      <th>sentimen_label_manual</th>\n",
       "      <th>vader_polarity</th>\n",
       "      <th>sentiment</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>1613078056104493056</td>\n",
       "      <td>If you are looking to get professional access ...</td>\n",
       "      <td>If you are looking to get professional access ...</td>\n",
       "      <td>Positive</td>\n",
       "      <td>{'neg': 0.0, 'neu': 1.0, 'pos': 0.0, 'compound...</td>\n",
       "      <td>Neutral</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1631035154633457664</td>\n",
       "      <td>I used ChatGPT to write a contract for me and ...</td>\n",
       "      <td>I used ChatGPT to write a contract for me and ...</td>\n",
       "      <td>Positive</td>\n",
       "      <td>{'neg': 0.0, 'neu': 0.852, 'pos': 0.148, 'comp...</td>\n",
       "      <td>Positive</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>1599824753417560064</td>\n",
       "      <td>what happens when these chatGPT systems fail i...</td>\n",
       "      <td>what happens when these chatGPT systems fail i...</td>\n",
       "      <td>Negative</td>\n",
       "      <td>{'neg': 0.216, 'neu': 0.651, 'pos': 0.134, 'co...</td>\n",
       "      <td>Negative</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   index             tweet_id  \\\n",
       "0      0  1613078056104493056   \n",
       "1      1  1631035154633457664   \n",
       "2      2  1599824753417560064   \n",
       "\n",
       "                                       tweet_content  \\\n",
       "0  If you are looking to get professional access ...   \n",
       "1  I used ChatGPT to write a contract for me and ...   \n",
       "2  what happens when these chatGPT systems fail i...   \n",
       "\n",
       "                      tweet_content_sentiment_prepro sentimen_label_manual  \\\n",
       "0  If you are looking to get professional access ...              Positive   \n",
       "1  I used ChatGPT to write a contract for me and ...              Positive   \n",
       "2  what happens when these chatGPT systems fail i...              Negative   \n",
       "\n",
       "                                      vader_polarity sentiment  \n",
       "0  {'neg': 0.0, 'neu': 1.0, 'pos': 0.0, 'compound...   Neutral  \n",
       "1  {'neg': 0.0, 'neu': 0.852, 'pos': 0.148, 'comp...  Positive  \n",
       "2  {'neg': 0.216, 'neu': 0.651, 'pos': 0.134, 'co...  Negative  "
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "##Assign polarity categories\n",
    "polarity_assignement(results)\n",
    "results['sentimen_label_manual']=results['sentimen_label_manual'].map(manual_vals)\n",
    "results.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "    Positive       0.58      0.68      0.63       114\n",
      "    Negative       0.50      0.31      0.38        68\n",
      "     Neutral       0.47      0.53      0.50        68\n",
      "\n",
      "    accuracy                           0.54       250\n",
      "   macro avg       0.52      0.50      0.50       250\n",
      "weighted avg       0.53      0.54      0.53       250\n",
      "\n"
     ]
    }
   ],
   "source": [
    "#Asses VADER performance\n",
    "y_true = results['sentimen_label_manual']\n",
    "y_pred = results['sentiment']\n",
    "print(classification_report(y_true, y_pred, labels=['Positive', 'Negative', 'Neutral']))\n",
    " "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Get polarity scores for entire dataset\n",
    "sentiment_processed_df['vader_polarity'] = sentiment_processed_df['tweet_content_sentiment_prepro'].apply(lambda x: analyzer.polarity_scores(x))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\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>tweet_id</th>\n",
       "      <th>tweet_content</th>\n",
       "      <th>tweet_content_sentiment_prepro</th>\n",
       "      <th>vader_polarity</th>\n",
       "      <th>sentiment</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1644446066748854281</td>\n",
       "      <td>What's happening, it asks, and I reply that ch...</td>\n",
       "      <td>What's happening, it asks, and I reply that ch...</td>\n",
       "      <td>{'neg': 0.0, 'neu': 0.88, 'pos': 0.12, 'compou...</td>\n",
       "      <td>Positive</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1644442136664764444</td>\n",
       "      <td>...one in the depot is approximately 17,42 MB ...</td>\n",
       "      <td>...one in the depot is approximately , MB in s...</td>\n",
       "      <td>{'neg': 0.0, 'neu': 1.0, 'pos': 0.0, 'compound...</td>\n",
       "      <td>Neutral</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1644437753721942044</td>\n",
       "      <td>I’ve converted to chatgpt</td>\n",
       "      <td>I’ve converted to chatgpt</td>\n",
       "      <td>{'neg': 0.0, 'neu': 1.0, 'pos': 0.0, 'compound...</td>\n",
       "      <td>Neutral</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1644433541827280896</td>\n",
       "      <td>Asked #chatgpt if someone has a BMI of 29, are...</td>\n",
       "      <td>Asked chatgpt if someone has a BMI of , are th...</td>\n",
       "      <td>{'neg': 0.036, 'neu': 0.964, 'pos': 0.0, 'comp...</td>\n",
       "      <td>Negative</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1644429028307406849</td>\n",
       "      <td>How does ChatGPT train its AI using advanced h...</td>\n",
       "      <td>How does ChatGPT train its AI using advanced h...</td>\n",
       "      <td>{'neg': 0.0, 'neu': 0.939, 'pos': 0.061, 'comp...</td>\n",
       "      <td>Positive</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1640041</th>\n",
       "      <td>1632455286211371009</td>\n",
       "      <td>The future of marketing collaboration: Coca-Co...</td>\n",
       "      <td>The future of marketing collaboration: Coca-Co...</td>\n",
       "      <td>{'neg': 0.0, 'neu': 1.0, 'pos': 0.0, 'compound...</td>\n",
       "      <td>Neutral</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1640042</th>\n",
       "      <td>1632455296441360386</td>\n",
       "      <td>@AwanishSharan Question Forwarded to #ChatGPT ...</td>\n",
       "      <td>Question Forwarded to Chat G P T . Waiting For...</td>\n",
       "      <td>{'neg': 0.091, 'neu': 0.715, 'pos': 0.195, 'co...</td>\n",
       "      <td>Positive</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1640043</th>\n",
       "      <td>1632455301701189632</td>\n",
       "      <td>Great experiment, fascinating reply from Chat ...</td>\n",
       "      <td>Great experiment, fascinating reply from Chat ...</td>\n",
       "      <td>{'neg': 0.0, 'neu': 0.397, 'pos': 0.603, 'comp...</td>\n",
       "      <td>Positive</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1640044</th>\n",
       "      <td>1632455315005509632</td>\n",
       "      <td>Focus on the #chatGPT prompts: ‘There’s so muc...</td>\n",
       "      <td>Focus on the chat G P T prompts: ‘There’s so m...</td>\n",
       "      <td>{'neg': 0.0, 'neu': 0.737, 'pos': 0.263, 'comp...</td>\n",
       "      <td>Positive</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1640045</th>\n",
       "      <td>1632455352922021890</td>\n",
       "      <td>Question Forwarded to #ChatGPT .\\n\\nWaiting Fo...</td>\n",
       "      <td>Question Forwarded to Chat G P T . Waiting For...</td>\n",
       "      <td>{'neg': 0.091, 'neu': 0.715, 'pos': 0.195, 'co...</td>\n",
       "      <td>Positive</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1640046 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                    tweet_id  \\\n",
       "0        1644446066748854281   \n",
       "1        1644442136664764444   \n",
       "2        1644437753721942044   \n",
       "3        1644433541827280896   \n",
       "4        1644429028307406849   \n",
       "...                      ...   \n",
       "1640041  1632455286211371009   \n",
       "1640042  1632455296441360386   \n",
       "1640043  1632455301701189632   \n",
       "1640044  1632455315005509632   \n",
       "1640045  1632455352922021890   \n",
       "\n",
       "                                             tweet_content  \\\n",
       "0        What's happening, it asks, and I reply that ch...   \n",
       "1        ...one in the depot is approximately 17,42 MB ...   \n",
       "2                                I’ve converted to chatgpt   \n",
       "3        Asked #chatgpt if someone has a BMI of 29, are...   \n",
       "4        How does ChatGPT train its AI using advanced h...   \n",
       "...                                                    ...   \n",
       "1640041  The future of marketing collaboration: Coca-Co...   \n",
       "1640042  @AwanishSharan Question Forwarded to #ChatGPT ...   \n",
       "1640043  Great experiment, fascinating reply from Chat ...   \n",
       "1640044  Focus on the #chatGPT prompts: ‘There’s so muc...   \n",
       "1640045  Question Forwarded to #ChatGPT .\\n\\nWaiting Fo...   \n",
       "\n",
       "                            tweet_content_sentiment_prepro  \\\n",
       "0        What's happening, it asks, and I reply that ch...   \n",
       "1        ...one in the depot is approximately , MB in s...   \n",
       "2                                I’ve converted to chatgpt   \n",
       "3        Asked chatgpt if someone has a BMI of , are th...   \n",
       "4        How does ChatGPT train its AI using advanced h...   \n",
       "...                                                    ...   \n",
       "1640041  The future of marketing collaboration: Coca-Co...   \n",
       "1640042  Question Forwarded to Chat G P T . Waiting For...   \n",
       "1640043  Great experiment, fascinating reply from Chat ...   \n",
       "1640044  Focus on the chat G P T prompts: ‘There’s so m...   \n",
       "1640045  Question Forwarded to Chat G P T . Waiting For...   \n",
       "\n",
       "                                            vader_polarity sentiment  \n",
       "0        {'neg': 0.0, 'neu': 0.88, 'pos': 0.12, 'compou...  Positive  \n",
       "1        {'neg': 0.0, 'neu': 1.0, 'pos': 0.0, 'compound...   Neutral  \n",
       "2        {'neg': 0.0, 'neu': 1.0, 'pos': 0.0, 'compound...   Neutral  \n",
       "3        {'neg': 0.036, 'neu': 0.964, 'pos': 0.0, 'comp...  Negative  \n",
       "4        {'neg': 0.0, 'neu': 0.939, 'pos': 0.061, 'comp...  Positive  \n",
       "...                                                    ...       ...  \n",
       "1640041  {'neg': 0.0, 'neu': 1.0, 'pos': 0.0, 'compound...   Neutral  \n",
       "1640042  {'neg': 0.091, 'neu': 0.715, 'pos': 0.195, 'co...  Positive  \n",
       "1640043  {'neg': 0.0, 'neu': 0.397, 'pos': 0.603, 'comp...  Positive  \n",
       "1640044  {'neg': 0.0, 'neu': 0.737, 'pos': 0.263, 'comp...  Positive  \n",
       "1640045  {'neg': 0.091, 'neu': 0.715, 'pos': 0.195, 'co...  Positive  \n",
       "\n",
       "[1640046 rows x 5 columns]"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Assign polarity category\n",
    "polarity_assignement(sentiment_processed_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Merge to main dataframe and save results to Tweets_SentimentLabelled_PreLDA.csv\n",
    "#sentiment_df.merge(sentiment_processed_df[['tweet_id','tweet_content_sentiment_prepro','vader_polarity','sentiment']],on='tweet_id').to_csv('Tweets_SentimentLabelled_PreLDA.csv',index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "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>tweet_id</th>\n",
       "      <th>tweet_content</th>\n",
       "      <th>tweet_content_hashtags</th>\n",
       "      <th>tweet_content_mentions</th>\n",
       "      <th>tweet_content_URLs</th>\n",
       "      <th>tweet_content_emoji</th>\n",
       "      <th>tweet_content_clean</th>\n",
       "      <th>tweet_content_tokens</th>\n",
       "      <th>tweet_content_tokens_no_verbs</th>\n",
       "      <th>tweet_content_sentiment_prepro</th>\n",
       "      <th>vader_polarity</th>\n",
       "      <th>sentiment</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1644446066748854281</td>\n",
       "      <td>What's happening, it asks, and I reply that ch...</td>\n",
       "      <td>['teaching']</td>\n",
       "      <td>[]</td>\n",
       "      <td>[]</td>\n",
       "      <td>NaN</td>\n",
       "      <td>whats happening it asks and i reply that chat ...</td>\n",
       "      <td>['class', 'kind', 'world', 'english', 'alterna...</td>\n",
       "      <td>['class', 'kind', 'world', 'english', 'alterna...</td>\n",
       "      <td>What's happening, it asks, and I reply that ch...</td>\n",
       "      <td>{'neg': 0.0, 'neu': 0.88, 'pos': 0.12, 'compou...</td>\n",
       "      <td>Positive</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1644442136664764444</td>\n",
       "      <td>...one in the depot is approximately 17,42 MB ...</td>\n",
       "      <td>[]</td>\n",
       "      <td>[]</td>\n",
       "      <td>[]</td>\n",
       "      <td>NaN</td>\n",
       "      <td>one in the depot is approximately mb in size t...</td>\n",
       "      <td>['correct', 'size', 'chatgpt', 'depot', 'curre...</td>\n",
       "      <td>['correct', 'size', 'chatgpt', 'depot', 'curre...</td>\n",
       "      <td>...one in the depot is approximately , MB in s...</td>\n",
       "      <td>{'neg': 0.0, 'neu': 1.0, 'pos': 0.0, 'compound...</td>\n",
       "      <td>Neutral</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1644437753721942044</td>\n",
       "      <td>I’ve converted to chatgpt</td>\n",
       "      <td>[]</td>\n",
       "      <td>[]</td>\n",
       "      <td>[]</td>\n",
       "      <td>NaN</td>\n",
       "      <td>ive converted to chatgpt</td>\n",
       "      <td>['chatgpt']</td>\n",
       "      <td>['chatgpt']</td>\n",
       "      <td>I’ve converted to chatgpt</td>\n",
       "      <td>{'neg': 0.0, 'neu': 1.0, 'pos': 0.0, 'compound...</td>\n",
       "      <td>Neutral</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              tweet_id                                      tweet_content  \\\n",
       "0  1644446066748854281  What's happening, it asks, and I reply that ch...   \n",
       "1  1644442136664764444  ...one in the depot is approximately 17,42 MB ...   \n",
       "2  1644437753721942044                          I’ve converted to chatgpt   \n",
       "\n",
       "  tweet_content_hashtags tweet_content_mentions tweet_content_URLs  \\\n",
       "0           ['teaching']                     []                 []   \n",
       "1                     []                     []                 []   \n",
       "2                     []                     []                 []   \n",
       "\n",
       "  tweet_content_emoji                                tweet_content_clean  \\\n",
       "0                 NaN  whats happening it asks and i reply that chat ...   \n",
       "1                 NaN  one in the depot is approximately mb in size t...   \n",
       "2                 NaN                           ive converted to chatgpt   \n",
       "\n",
       "                                tweet_content_tokens  \\\n",
       "0  ['class', 'kind', 'world', 'english', 'alterna...   \n",
       "1  ['correct', 'size', 'chatgpt', 'depot', 'curre...   \n",
       "2                                        ['chatgpt']   \n",
       "\n",
       "                       tweet_content_tokens_no_verbs  \\\n",
       "0  ['class', 'kind', 'world', 'english', 'alterna...   \n",
       "1  ['correct', 'size', 'chatgpt', 'depot', 'curre...   \n",
       "2                                        ['chatgpt']   \n",
       "\n",
       "                      tweet_content_sentiment_prepro  \\\n",
       "0  What's happening, it asks, and I reply that ch...   \n",
       "1  ...one in the depot is approximately , MB in s...   \n",
       "2                          I’ve converted to chatgpt   \n",
       "\n",
       "                                      vader_polarity sentiment  \n",
       "0  {'neg': 0.0, 'neu': 0.88, 'pos': 0.12, 'compou...  Positive  \n",
       "1  {'neg': 0.0, 'neu': 1.0, 'pos': 0.0, 'compound...   Neutral  \n",
       "2  {'neg': 0.0, 'neu': 1.0, 'pos': 0.0, 'compound...   Neutral  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Read results\n",
    "resulting_df= pd.read_csv('data_files/Tweets_SentimentLabelled_PreLDA.csv')\n",
    "resulting_df['tweet_id']=resulting_df['tweet_id'].astype(str)\n",
    "resulting_df['vader_polarity'] = resulting_df['vader_polarity'].apply(lambda x: ast.literal_eval(x))\n",
    "resulting_df.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "resulting_df['compound']= resulting_df['vader_polarity'].apply(lambda x:x['compound'] )\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "resulting_df.to_csv('data_files/Tweets_SentimentLabelled_PreLDA.csv',index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "resulting_df= pd.read_csv('data_files/Tweets_SentimentLabelled_PreLDA.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1640046, 13)"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "resulting_df.shape"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Tweets Topic Modelling"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This notebook sections centers on identifying common topics of discussion on the tweets about ChatGPT. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "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>tweet_id</th>\n",
       "      <th>tweet_content</th>\n",
       "      <th>tweet_content_hashtags</th>\n",
       "      <th>tweet_content_mentions</th>\n",
       "      <th>tweet_content_URLs</th>\n",
       "      <th>tweet_content_emoji</th>\n",
       "      <th>tweet_content_clean</th>\n",
       "      <th>tweet_content_tokens</th>\n",
       "      <th>tweet_content_tokens_no_verbs</th>\n",
       "      <th>tweet_content_sentiment_prepro</th>\n",
       "      <th>vader_polarity</th>\n",
       "      <th>sentiment</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1640043</th>\n",
       "      <td>1632455301701189632</td>\n",
       "      <td>Great experiment, fascinating reply from Chat ...</td>\n",
       "      <td>[]</td>\n",
       "      <td>[]</td>\n",
       "      <td>[]</td>\n",
       "      <td>NaN</td>\n",
       "      <td>great experiment fascinating reply from chat gpt</td>\n",
       "      <td>[reply, fascinating, chat, experiment, great, ...</td>\n",
       "      <td>[experiment, great]</td>\n",
       "      <td>Great experiment, fascinating reply from Chat ...</td>\n",
       "      <td>{'neg': 0.0, 'neu': 0.397, 'pos': 0.603, 'comp...</td>\n",
       "      <td>Positive</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1640044</th>\n",
       "      <td>1632455315005509632</td>\n",
       "      <td>Focus on the #chatGPT prompts: ‘There’s so muc...</td>\n",
       "      <td>[chatGPT, resilientpedagogy, edtech]</td>\n",
       "      <td>[]</td>\n",
       "      <td>['https://t.co/nqUiXUFhdN']</td>\n",
       "      <td>NaN</td>\n",
       "      <td>focus on the prompts theres so much more this ...</td>\n",
       "      <td>[time, well, save, prompt, employ, creativity,...</td>\n",
       "      <td>[example, prompt, well, creativity, focus, emp...</td>\n",
       "      <td>Focus on the chat G P T prompts: ‘There’s so m...</td>\n",
       "      <td>{'neg': 0.0, 'neu': 0.737, 'pos': 0.263, 'comp...</td>\n",
       "      <td>Positive</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1640045</th>\n",
       "      <td>1632455352922021890</td>\n",
       "      <td>Question Forwarded to #ChatGPT .\\n\\nWaiting Fo...</td>\n",
       "      <td>[ChatGPT]</td>\n",
       "      <td>[]</td>\n",
       "      <td>[]</td>\n",
       "      <td>😂</td>\n",
       "      <td>question forwarded to waiting for response</td>\n",
       "      <td>[forward, wait, response, question]</td>\n",
       "      <td>[response, forward, question]</td>\n",
       "      <td>Question Forwarded to Chat G P T . Waiting For...</td>\n",
       "      <td>{'neg': 0.091, 'neu': 0.715, 'pos': 0.195, 'co...</td>\n",
       "      <td>Positive</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    tweet_id  \\\n",
       "1640043  1632455301701189632   \n",
       "1640044  1632455315005509632   \n",
       "1640045  1632455352922021890   \n",
       "\n",
       "                                             tweet_content  \\\n",
       "1640043  Great experiment, fascinating reply from Chat ...   \n",
       "1640044  Focus on the #chatGPT prompts: ‘There’s so muc...   \n",
       "1640045  Question Forwarded to #ChatGPT .\\n\\nWaiting Fo...   \n",
       "\n",
       "                       tweet_content_hashtags tweet_content_mentions  \\\n",
       "1640043                                    []                     []   \n",
       "1640044  [chatGPT, resilientpedagogy, edtech]                     []   \n",
       "1640045                             [ChatGPT]                     []   \n",
       "\n",
       "                  tweet_content_URLs tweet_content_emoji  \\\n",
       "1640043                           []                 NaN   \n",
       "1640044  ['https://t.co/nqUiXUFhdN']                 NaN   \n",
       "1640045                           []                   😂   \n",
       "\n",
       "                                       tweet_content_clean  \\\n",
       "1640043   great experiment fascinating reply from chat gpt   \n",
       "1640044  focus on the prompts theres so much more this ...   \n",
       "1640045        question forwarded to waiting for response    \n",
       "\n",
       "                                      tweet_content_tokens  \\\n",
       "1640043  [reply, fascinating, chat, experiment, great, ...   \n",
       "1640044  [time, well, save, prompt, employ, creativity,...   \n",
       "1640045                [forward, wait, response, question]   \n",
       "\n",
       "                             tweet_content_tokens_no_verbs  \\\n",
       "1640043                                [experiment, great]   \n",
       "1640044  [example, prompt, well, creativity, focus, emp...   \n",
       "1640045                      [response, forward, question]   \n",
       "\n",
       "                            tweet_content_sentiment_prepro  \\\n",
       "1640043  Great experiment, fascinating reply from Chat ...   \n",
       "1640044  Focus on the chat G P T prompts: ‘There’s so m...   \n",
       "1640045  Question Forwarded to Chat G P T . Waiting For...   \n",
       "\n",
       "                                            vader_polarity sentiment  \n",
       "1640043  {'neg': 0.0, 'neu': 0.397, 'pos': 0.603, 'comp...  Positive  \n",
       "1640044  {'neg': 0.0, 'neu': 0.737, 'pos': 0.263, 'comp...  Positive  \n",
       "1640045  {'neg': 0.091, 'neu': 0.715, 'pos': 0.195, 'co...  Positive  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Load data\n",
    "tweets_df = pd.read_csv('data_files/Tweets_SentimentLabelled_PreLDA.csv')\n",
    "\n",
    "#Load columns that have lists values. These are initially read as string and need to be casted\n",
    "for col in ['tweet_content_tokens','tweet_content_hashtags','tweet_content_tokens_no_verbs']:\n",
    "    #Since some of the tweets have no tokens we need to check if np.nan (float) is contained before splitting the tokens content\n",
    "    tweets_df[f'{col}'] = tweets_df[f'{col}'].apply(lambda x: x.strip(\"']['\").split(\"', '\") if not(isinstance(x, float)) else [])\n",
    "\n",
    "#user_prepro_df['usr_description_tokens'] = user_prepro_df['usr_description_tokens'].apply(lambda x: list(x).remove(str(np.nan)) if str(np.nan) in x else x)\n",
    "\n",
    "tweets_df.tail(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1640046, 12)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tweets_df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "#For the purpose of topic modelling only a few columns are required. For the next steps, only these will be considered\n",
    "tweets_prepro_df =  tweets_df[['tweet_id','tweet_content_hashtags','tweet_content_clean','tweet_content_tokens_no_verbs','tweet_content_tokens']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "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>tweet_id</th>\n",
       "      <th>tweet_content_hashtags</th>\n",
       "      <th>tweet_content_clean</th>\n",
       "      <th>tweet_content_tokens_no_verbs</th>\n",
       "      <th>tweet_content_tokens</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1644446066748854281</td>\n",
       "      <td>[teaching]</td>\n",
       "      <td>whats happening it asks and i reply that chat ...</td>\n",
       "      <td>[class, kind, world, english, alternative, com...</td>\n",
       "      <td>[class, kind, world, english, alternative, com...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1644442136664764444</td>\n",
       "      <td>[]</td>\n",
       "      <td>one in the depot is approximately mb in size t...</td>\n",
       "      <td>[correct, size, chatgpt, depot, current, ameri...</td>\n",
       "      <td>[correct, size, chatgpt, depot, current, ameri...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1644437753721942044</td>\n",
       "      <td>[]</td>\n",
       "      <td>ive converted to chatgpt</td>\n",
       "      <td>[chatgpt]</td>\n",
       "      <td>[chatgpt]</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              tweet_id tweet_content_hashtags  \\\n",
       "0  1644446066748854281             [teaching]   \n",
       "1  1644442136664764444                     []   \n",
       "2  1644437753721942044                     []   \n",
       "\n",
       "                                 tweet_content_clean  \\\n",
       "0  whats happening it asks and i reply that chat ...   \n",
       "1  one in the depot is approximately mb in size t...   \n",
       "2                           ive converted to chatgpt   \n",
       "\n",
       "                       tweet_content_tokens_no_verbs  \\\n",
       "0  [class, kind, world, english, alternative, com...   \n",
       "1  [correct, size, chatgpt, depot, current, ameri...   \n",
       "2                                          [chatgpt]   \n",
       "\n",
       "                                tweet_content_tokens  \n",
       "0  [class, kind, world, english, alternative, com...  \n",
       "1  [correct, size, chatgpt, depot, current, ameri...  \n",
       "2                                          [chatgpt]  "
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Inspect dataframe\n",
    "tweets_prepro_df.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Other stop words were also included, thus we will remove them \n",
    "extra_stop_words = ['r','rt','an','dm','le','yo','por','I','el','lo','con','zzzzzzzzz','aaaa','aaaaa','aaa','b',\n",
    "                    '',\"\",\"-\",\"also\" \"k\",\"c\",\"oth\",\"rth\",\n",
    "                    \"much\",\"many\",\"de\",\"e\",\"thing\", \"la\", \"prev\",\"e\",\n",
    "                    \"k\",\"et\",\"un\", str(np.nan), 'nan', 'NaN', 'gpt','apus','aus','amp', 'chatgpt','chatbot','bot'\n",
    "                    'ai','artificial','intelligence','lol','maybe','good','bad','sad', 'shall','really','bit',\n",
    "                    'even','also','still','today','yesterday','lot','less','more','seem','yet','back',\n",
    "                    'become','like','actually','put','gpt-',\n",
    "                    'must','needs','short','long','gon','agree','disagree','whole','miss','shit','fuck',\n",
    "                    'away','likely','unlikely','possible','impossible','literally','however','so','super',\n",
    "                    'often','ago','currently','now','non','quite','exactly','recently','entire',\n",
    "                    'cause','completly','especially','basically','fine','easily','dall','basic',\n",
    "                    'simply','absolutely','w','around','yesterday','happy',\n",
    "                    'fully','eg','honestly','mostly','anymore','anytime','v','x',\n",
    "                    'suck','seriously','al','apparently','extremely',\n",
    "                    'eventually','reportedly','specifically','particularly', 'next',\n",
    "                    'use','well','think','stuff', 'want','catch','wait','introduce',\n",
    "                    'see','know','sure','never','else','tell','nice','go','ad',\n",
    "                    'pro','anti','give','claim','find','dan','bet','stupid','engage','highly',\n",
    "                    'relate','dear', 'hour','month','may','might',\n",
    "                    'damn','u','definitely','one', 'try','do','hear','speak','say',\n",
    "                    'join','crazy','excellent','sad','dumb','scary','lazy','useless', 'love','hate',\n",
    "                    'great','funny','exceed','amazing', 'cool','sorry'] \n",
    "\n",
    "                   "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Remove extra stop words from tokens with no verbs\n",
    "tweets_prepro_df['tweet_content_tokens_no_verbs'] = tweets_prepro_df['tweet_content_tokens_no_verbs'].apply(lambda x: x if type(x) == type(None) else list(set(x).difference(extra_stop_words)))\n",
    "#Remove extra stop words from tokens with verbs\n",
    "tweets_prepro_df['tweet_content_tokens'] = tweets_prepro_df['tweet_content_tokens'].apply(lambda x: x if type(x) == type(None) else list(set(x).difference(extra_stop_words)))\n",
    "##Create new column where tweet topic will be stored\n",
    "tweets_prepro_df['tweet_topic'] = \"\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Investigate hashtags to see if they can be used in LDA. \n",
    "hashtags = WordCloud(colormap='hsv_r', \n",
    "                            background_color='white').generate(' '.join(itertools.chain.from_iterable(tweets_prepro_df['tweet_content_hashtags'])))\n",
    "plt.imshow(hashtags, interpolation='bilinear')\n",
    "plt.title('Hashtags')\n",
    "plt.axis(\"off\")\n",
    "plt.savefig('hashtags_viz.jpg')\n",
    "plt.show()\n",
    "#The results did not deliver satisfying results so they were not considered further."
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### LDA on tweet_content_tokens"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Create a corpus from from usr_description\n",
    "dictionary = Dictionary(tweets_prepro_df['tweet_content_tokens'])\n",
    "#filter out low-frequency / high-frequency tokens\n",
    "dictionary.filter_extremes(no_below=10, no_above=0.5)\n",
    "#finalize corpus from dict\n",
    "corpus = [dictionary.doc2bow(doc) for doc in tweets_prepro_df['tweet_content_tokens']]\n",
    "#corpus and dictionary refer to how many times a token (word) appears in two dimensions: once in the whole group of tokens for the entire dataset, and once for the specific tweet"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "#First instance LDA 10 topics before extra stop words were removed\n",
    "lda_model_base_including_verbs = LdaMulticore(corpus, id2word=dictionary, num_topics=10, workers = 7, passes=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "##SaveUSR LDA Model\n",
    "lda_model_base_including_verbs.save('Tweets_LDA/LDA_Base_Including_Verbs/lda_model_base_including_verbs')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "#load saved model\n",
    "lda_model_base_including_verbs = models.ldamodel.LdaModel.load('Tweets_LDA/LDA_Base_Including_Verbs/lda_model_base_including_verbs')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Coherence Score Base Model (including verbs):  0.4805706971715444\n"
     ]
    }
   ],
   "source": [
    "#Check coherence score of base model \n",
    "coherence_model_base_including_verbs = CoherenceModel(model=lda_model_base_including_verbs, texts=tweets_prepro_df['tweet_content_tokens'], dictionary=dictionary, coherence='c_v')\n",
    "coherence_score_base_including_verbs = coherence_model_base_including_verbs.get_coherence()\n",
    "print('Coherence Score Base Model (including verbs): ', coherence_score_base_including_verbs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[(0,\n",
       "  '0.019*\"bot\" + 0.013*\"elon\" + 0.011*\"musk\" + 0.009*\"ceo\" + 0.009*\"wake\" + 0.008*\"openai\" + 0.008*\"creator\" + 0.008*\"fake\" + 0.007*\"story\" + 0.007*\"bias\"'),\n",
       " (1,\n",
       "  '0.043*\"write\" + 0.025*\"read\" + 0.022*\"content\" + 0.022*\"article\" + 0.021*\"student\" + 0.015*\"generate\" + 0.014*\"tool\" + 0.012*\"book\" + 0.011*\"writing\" + 0.011*\"school\"'),\n",
       " (2,\n",
       "  '0.067*\"write\" + 0.064*\"ask\" + 0.058*\"tweet\" + 0.051*\"response\" + 0.039*\"limit\" + 0.035*\"character\" + 0.030*\"bard\" + 0.021*\"post\" + 0.020*\"twitter\" + 0.012*\"question\"'),\n",
       " (3,\n",
       "  '0.076*\"google\" + 0.044*\"search\" + 0.042*\"microsoft\" + 0.033*\"bing\" + 0.030*\"chatbot\" + 0.029*\"new\" + 0.025*\"openai\" + 0.022*\"power\" + 0.017*\"engine\" + 0.016*\"open\"'),\n",
       " (4,\n",
       "  '0.023*\"answer\" + 0.020*\"question\" + 0.017*\"ask\" + 0.014*\"human\" + 0.013*\"make\" + 0.011*\"get\" + 0.011*\"people\" + 0.008*\"wrong\" + 0.007*\"may\" + 0.007*\"information\"'),\n",
       " (5,\n",
       "  '0.017*\"week\" + 0.015*\"month\" + 0.014*\"plan\" + 0.014*\"take\" + 0.013*\"last\" + 0.013*\"day\" + 0.011*\"new\" + 0.011*\"app\" + 0.010*\"world\" + 0.009*\"late\"'),\n",
       " (6,\n",
       "  '0.162*\"chat\" + 0.028*\"get\" + 0.018*\"pay\" + 0.017*\"game\" + 0.016*\"talk\" + 0.016*\"ask\" + 0.013*\"right\" + 0.013*\"make\" + 0.012*\"friend\" + 0.011*\"mind\"'),\n",
       " (7,\n",
       "  '0.031*\"code\" + 0.022*\"prompt\" + 0.019*\"create\" + 0.019*\"help\" + 0.017*\"write\" + 0.016*\"work\" + 0.014*\"thread\" + 0.014*\"save\" + 0.014*\"make\" + 0.014*\"video\"'),\n",
       " (8,\n",
       "  '0.027*\"make\" + 0.027*\"job\" + 0.024*\"people\" + 0.022*\"take\" + 0.021*\"time\" + 0.020*\"replace\" + 0.018*\"thank\" + 0.017*\"year\" + 0.013*\"work\" + 0.011*\"come\"'),\n",
       " (9,\n",
       "  '0.025*\"model\" + 0.020*\"language\" + 0.017*\"tool\" + 0.016*\"technology\" + 0.012*\"data\" + 0.011*\"business\" + 0.010*\"openai\" + 0.010*\"new\" + 0.010*\"future\" + 0.010*\"generative\"')]"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Inspect clusters resulting from base LDA\n",
    "lda_model_base_including_verbs.print_topics()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Since the coherence score of the base model was only 0.48 the most popular 1000 words in the corpus will be checked and if relevant added to the *extra_stop_words list*."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "popular_words_dict={}\n",
    "\n",
    "for tweet_list in corpus:\n",
    "    for word_tuple in tweet_list:\n",
    "        word_id = word_tuple[0]\n",
    "        word_count = word_tuple[1]\n",
    "        if word_id in popular_words_dict:\n",
    "            popular_words_dict[word_id] = popular_words_dict[word_id] + word_count\n",
    "        else:\n",
    "            popular_words_dict[word_id] = word_count\n",
    "\n",
    "sorted_popular_dict=dict(sorted(popular_words_dict.items(), key=lambda item: item[1], reverse=True))\n",
    "most_popular_1000words = list(sorted_popular_dict.keys())[:1000]\n",
    "\n",
    "most_popular_1000words_text = []\n",
    "for word_id in most_popular_1000words:\n",
    "    most_popular_1000words_text.append(dictionary[word_id])\n",
    "    print(dictionary[word_id])\n",
    "\n",
    "\n",
    "#TODO: if needed remove hashtag\n",
    "#with open(\"top1000words.txt\", \"w\") as output:\n",
    "#   output.write(str(most_popular_1000words_text))"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### LDA on tweet_content_tokens_no_verbs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create a corpus from usr_description\n",
    "dictionary_no_verbs = Dictionary(tweets_prepro_df['tweet_content_tokens_no_verbs'])\n",
    "# filter out low-frequency / high-frequency stuff\n",
    "dictionary_no_verbs.filter_extremes(no_below=10, no_above=0.5)\n",
    "#finalize corpus from dictionary\n",
    "corpus_no_verbs = [dictionary_no_verbs.doc2bow(doc) for doc in tweets_prepro_df['tweet_content_tokens_no_verbs']]\n",
    "\n",
    "# corpus and dictionary refer to how many times a token (word) appears in two dimensions: once in the whole group of tokens for the entire dataset, and once for the specific tweet"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "#First instance LDA 10 topics before extra stop words were removed\n",
    "lda_model_base_no_verbs = LdaMulticore(corpus_no_verbs, id2word=dictionary_no_verbs, num_topics=10, workers = 7, passes=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "##Save LDA Model\n",
    "lda_model_base_no_verbs.save('Tweets_LDA/LDA_Base_Excluding_Verbs/lda_model_base_no_verbs')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Load LDA model with no verbs\n",
    "lda_model_base_no_verbs = models.ldamodel.LdaModel.load('Tweets_LDA/LDA_Base_Excluding_Verbs/lda_model_base_no_verbs')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Coherence Score Base Model (no verbs): 0.49747284319750146\n"
     ]
    }
   ],
   "source": [
    "#Check coherence score of initial model with no verbs\n",
    "coherence_model_base_no_verbs = CoherenceModel(model=lda_model_base_no_verbs, texts=tweets_prepro_df['tweet_content_tokens_no_verbs'], dictionary=dictionary_no_verbs, coherence='c_v')\n",
    "coherence_score_base_no_verbs = coherence_model_base_no_verbs.get_coherence()\n",
    "print('Coherence Score Base Model (no verbs):', coherence_score_base_no_verbs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[(0,\n",
       "  '0.047*\"write\" + 0.034*\"student\" + 0.018*\"school\" + 0.016*\"essay\" + 0.014*\"exam\" + 0.014*\"teacher\" + 0.013*\"friend\" + 0.013*\"paper\" + 0.010*\"new\" + 0.010*\"education\"'),\n",
       " (1,\n",
       "  '0.034*\"model\" + 0.028*\"language\" + 0.026*\"data\" + 0.018*\"text\" + 0.016*\"base\" + 0.013*\"train\" + 0.010*\"human\" + 0.010*\"tool\" + 0.009*\"large\" + 0.009*\"generate\"'),\n",
       " (2,\n",
       "  '0.065*\"chat\" + 0.042*\"code\" + 0.028*\"work\" + 0.017*\"time\" + 0.016*\"thank\" + 0.015*\"video\" + 0.013*\"help\" + 0.011*\"problem\" + 0.010*\"prompt\" + 0.009*\"right\"'),\n",
       " (3,\n",
       "  '0.067*\"google\" + 0.054*\"microsoft\" + 0.050*\"search\" + 0.043*\"new\" + 0.028*\"bing\" + 0.023*\"app\" + 0.022*\"engine\" + 0.021*\"power\" + 0.014*\"thread\" + 0.014*\"openai\"'),\n",
       " (4,\n",
       "  '0.027*\"world\" + 0.026*\"job\" + 0.023*\"future\" + 0.017*\"change\" + 0.017*\"technology\" + 0.013*\"new\" + 0.013*\"life\" + 0.013*\"way\" + 0.010*\"mind\" + 0.010*\"potential\"'),\n",
       " (5,\n",
       "  '0.033*\"openai\" + 0.022*\"free\" + 0.022*\"article\" + 0.021*\"chatbot\" + 0.021*\"content\" + 0.021*\"post\" + 0.020*\"tool\" + 0.018*\"new\" + 0.015*\"late\" + 0.013*\"news\"'),\n",
       " (6,\n",
       "  '0.029*\"people\" + 0.025*\"human\" + 0.013*\"may\" + 0.010*\"bias\" + 0.009*\"fact\" + 0.009*\"word\" + 0.009*\"point\" + 0.007*\"already\" + 0.007*\"way\" + 0.007*\"work\"'),\n",
       " (7,\n",
       "  '0.026*\"bard\" + 0.022*\"google\" + 0.021*\"company\" + 0.018*\"month\" + 0.017*\"plan\" + 0.016*\"tech\" + 0.015*\"market\" + 0.014*\"medium\" + 0.013*\"rival\" + 0.013*\"elon\"'),\n",
       " (8,\n",
       "  '0.017*\"write\" + 0.016*\"day\" + 0.012*\"talk\" + 0.011*\"bot\" + 0.010*\"time\" + 0.010*\"last\" + 0.009*\"guy\" + 0.009*\"name\" + 0.009*\"man\" + 0.009*\"people\"'),\n",
       " (9,\n",
       "  '0.053*\"question\" + 0.049*\"response\" + 0.049*\"answer\" + 0.029*\"tweet\" + 0.028*\"limit\" + 0.027*\"character\" + 0.020*\"ask\" + 0.018*\"prompt\" + 0.011*\"information\" + 0.011*\"result\"')]"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lda_model_base_no_verbs.print_topics()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## LDA Tuning"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "After creating the LDA base models on both columns storing tokens further improved models were be explored. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Function to compute coherence for LDA models with different variables\n",
    "def compute_coherence(path,file_version, models_dict, corpus_, dictionary_,text, iteration_list, alpha_list, eta_list, limit = 1,start=6,step=2):\n",
    "    dict_object = {'model':[],'coherence':[],'topics_no':[],'alpha':[],'eta':[],'no_iterations':[]}\n",
    "    \n",
    "    for iteration in iteration_list:\n",
    "        for alpha in alpha_list: \n",
    "            for eta in eta_list:\n",
    "                for num_topics in range(start,limit,step):\n",
    "                    print('Running...')\n",
    "\n",
    "                    model= LdaMulticore(corpus_, id2word=dictionary_, num_topics=num_topics, workers = 7, passes=10, alpha=alpha, iterations= iteration)\n",
    "                    coherence_model = CoherenceModel(model=model, texts=text, dictionary=dictionary_, coherence='c_v')\n",
    "                    \n",
    "                    dict_object = {'model':[model],'coherence':[coherence_model.get_coherence()],'topics_no':[num_topics],'alpha':[alpha],'eta':[eta],'no_iterations':[iteration]}\n",
    "                    model.save(f'{path}/tweetsLDA{len(models_dict)}{file_version}')\n",
    "                    pd.DataFrame.from_dict(dict_object).to_csv(f'{path}/{file_version}.csv', mode='a',header=False, index=False)\n",
    "\n",
    "                    models_dict.update({len(models_dict): dict_object})\n",
    "                    \n",
    "                    ##Save LDA Models in case it crushes\n",
    "                    # ct stores current time\n",
    "                    ct = datetime.datetime.now()\n",
    "                    print(f\"time: {ct}\")\n",
    "                    print(f\"Model idx{len(models_dict)}: Coherence:{coherence_model.get_coherence()}, TopicsNo.:{num_topics}, Alpha: {alpha}, Eta: {eta}, IterationsNo.: {iteration}\")\n",
    "                \n",
    "    return models_dict\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Dict of coherence scores\n",
    "models_with_verbs_dict = {}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#See coherence for including verbs\n",
    "compute_coherence('replacement_lda/','including_verbs', models_with_verbs_dict, corpus, dictionary,tweets_prepro_df['tweet_content_tokens'], [150,100,50], [0.03,0.5,'asymmetric'], ['auto'], limit = 16,start=6,step=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "        vertical-align: middle;\n",
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       "\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>model</th>\n",
       "      <th>coherence_score</th>\n",
       "      <th>k_topics</th>\n",
       "      <th>alpha</th>\n",
       "      <th>beta</th>\n",
       "      <th>no_iterations</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>LdaMulticore&lt;num_terms=28983, num_topics=10, d...</td>\n",
       "      <td>0.536066</td>\n",
       "      <td>10</td>\n",
       "      <td>0.03</td>\n",
       "      <td>auto</td>\n",
       "      <td>100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>LdaMulticore&lt;num_terms=28983, num_topics=12, d...</td>\n",
       "      <td>0.529101</td>\n",
       "      <td>12</td>\n",
       "      <td>0.03</td>\n",
       "      <td>auto</td>\n",
       "      <td>150</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>LdaMulticore&lt;num_terms=28983, num_topics=14, d...</td>\n",
       "      <td>0.512809</td>\n",
       "      <td>14</td>\n",
       "      <td>0.03</td>\n",
       "      <td>auto</td>\n",
       "      <td>150</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>LdaMulticore&lt;num_terms=28983, num_topics=12, d...</td>\n",
       "      <td>0.511342</td>\n",
       "      <td>12</td>\n",
       "      <td>0.03</td>\n",
       "      <td>auto</td>\n",
       "      <td>100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>LdaMulticore&lt;num_terms=28983, num_topics=10, d...</td>\n",
       "      <td>0.511012</td>\n",
       "      <td>10</td>\n",
       "      <td>asymmetric</td>\n",
       "      <td>auto</td>\n",
       "      <td>100</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                model  coherence_score  \\\n",
       "12  LdaMulticore<num_terms=28983, num_topics=10, d...         0.536066   \n",
       "1   LdaMulticore<num_terms=28983, num_topics=12, d...         0.529101   \n",
       "2   LdaMulticore<num_terms=28983, num_topics=14, d...         0.512809   \n",
       "13  LdaMulticore<num_terms=28983, num_topics=12, d...         0.511342   \n",
       "20  LdaMulticore<num_terms=28983, num_topics=10, d...         0.511012   \n",
       "\n",
       "    k_topics       alpha  beta  no_iterations  \n",
       "12        10        0.03  auto            100  \n",
       "1         12        0.03  auto            150  \n",
       "2         14        0.03  auto            150  \n",
       "13        12        0.03  auto            100  \n",
       "20        10  asymmetric  auto            100  "
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lda_including_verbs_df = pd.read_csv('data_files/Tweets_LDA/LDA_Base_Tuning_Verbs/including_verbs.csv', names=['model','coherence_score','k_topics','alpha','beta','no_iterations'])\n",
    "#Check highest coherence score of LDAs including verbs\n",
    "lda_including_verbs_df.sort_values(by='coherence_score',ascending=False).head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Dict of coherence score\n",
    "models_no_verbs_dict={}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Compute coherence scores of LDA based on tokens excludings verbs\n",
    "compute_coherence('Tweets_LDA/LDA_Base_Tuning_NO_Verbs/','excluding_verbs', models_no_verbs_dict, corpus_no_verbs, dictionary_no_verbs,tweets_prepro_df['tweet_content_tokens_no_verbs'], [150,100,50], [0.03,0.5,'asymmetric'], ['auto'], limit = 16,start=6,step=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "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>model</th>\n",
       "      <th>coherence_score</th>\n",
       "      <th>k_topics</th>\n",
       "      <th>alpha</th>\n",
       "      <th>beta</th>\n",
       "      <th>no_iterations</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>LdaMulticore&lt;num_terms=27547, num_topics=8, de...</td>\n",
       "      <td>0.509841</td>\n",
       "      <td>8</td>\n",
       "      <td>0.03</td>\n",
       "      <td>auto</td>\n",
       "      <td>100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>LdaMulticore&lt;num_terms=27547, num_topics=10, d...</td>\n",
       "      <td>0.503334</td>\n",
       "      <td>10</td>\n",
       "      <td>0.03</td>\n",
       "      <td>auto</td>\n",
       "      <td>100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>LdaMulticore&lt;num_terms=27547, num_topics=6, de...</td>\n",
       "      <td>0.502560</td>\n",
       "      <td>6</td>\n",
       "      <td>asymmetric</td>\n",
       "      <td>auto</td>\n",
       "      <td>150</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>LdaMulticore&lt;num_terms=27547, num_topics=14, d...</td>\n",
       "      <td>0.502221</td>\n",
       "      <td>14</td>\n",
       "      <td>0.03</td>\n",
       "      <td>auto</td>\n",
       "      <td>50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>LdaMulticore&lt;num_terms=27547, num_topics=12, d...</td>\n",
       "      <td>0.498602</td>\n",
       "      <td>12</td>\n",
       "      <td>0.03</td>\n",
       "      <td>auto</td>\n",
       "      <td>100</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                model  coherence_score  \\\n",
       "16  LdaMulticore<num_terms=27547, num_topics=8, de...         0.509841   \n",
       "17  LdaMulticore<num_terms=27547, num_topics=10, d...         0.503334   \n",
       "10  LdaMulticore<num_terms=27547, num_topics=6, de...         0.502560   \n",
       "34  LdaMulticore<num_terms=27547, num_topics=14, d...         0.502221   \n",
       "18  LdaMulticore<num_terms=27547, num_topics=12, d...         0.498602   \n",
       "\n",
       "    k_topics       alpha  beta  no_iterations  \n",
       "16         8        0.03  auto            100  \n",
       "17        10        0.03  auto            100  \n",
       "10         6  asymmetric  auto            150  \n",
       "34        14        0.03  auto             50  \n",
       "18        12        0.03  auto            100  "
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lda_including_no_verbs_df = pd.read_csv('data_files/LDA_CLEANSLATE/LDA_Base_Tuning_NO_Verbs/excluding_verbs.csv', names=['model','coherence_score','k_topics','alpha','beta','no_iterations'])\n",
    "#Check highest coherence of LDAs basde on tokens excluding verbs\n",
    "lda_including_no_verbs_df.sort_values(by='coherence_score',ascending=False).head(5)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The LDA model recording the highest coherence score will be inspected."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Loading chosen LDA model : k = 10, alpha = 0.03, beta = auto, no_iterations=100, coherence score = 0.527299\n",
    "tweet_topic_LDA = models.ldamodel.LdaModel.load('Tweets_LDA/LDA_Base_Tuning_Verbs/tweetsLDA12including_verbs')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[(0,\n",
       "  '0.034*\"student\" + 0.026*\"write\" + 0.017*\"school\" + 0.017*\"essay\" + 0.015*\"pass\" + 0.015*\"education\" + 0.014*\"exam\" + 0.014*\"teacher\" + 0.014*\"paper\" + 0.013*\"teach\"'),\n",
       " (1,\n",
       "  '0.040*\"ask\" + 0.030*\"answer\" + 0.027*\"question\" + 0.021*\"code\" + 0.013*\"make\" + 0.010*\"problem\" + 0.009*\"wrong\" + 0.008*\"write\" + 0.008*\"need\" + 0.007*\"work\"'),\n",
       " (2,\n",
       "  '0.053*\"write\" + 0.019*\"make\" + 0.015*\"prompt\" + 0.015*\"thread\" + 0.015*\"save\" + 0.015*\"help\" + 0.015*\"generate\" + 0.015*\"post\" + 0.015*\"ask\" + 0.013*\"work\"'),\n",
       " (3,\n",
       "  '0.018*\"chat\" + 0.016*\"make\" + 0.016*\"people\" + 0.016*\"time\" + 0.012*\"ask\" + 0.012*\"talk\" + 0.011*\"year\" + 0.011*\"write\" + 0.011*\"day\" + 0.010*\"come\"'),\n",
       " (4,\n",
       "  '0.028*\"model\" + 0.023*\"language\" + 0.016*\"human\" + 0.015*\"tool\" + 0.011*\"data\" + 0.010*\"learn\" + 0.010*\"train\" + 0.009*\"improve\" + 0.009*\"text\" + 0.008*\"base\"'),\n",
       " (5,\n",
       "  '0.061*\"google\" + 0.035*\"search\" + 0.033*\"microsoft\" + 0.026*\"bing\" + 0.025*\"openai\" + 0.021*\"new\" + 0.018*\"bard\" + 0.017*\"launch\" + 0.015*\"power\" + 0.013*\"engine\"'),\n",
       " (6,\n",
       "  '0.087*\"tweet\" + 0.070*\"response\" + 0.059*\"limit\" + 0.053*\"character\" + 0.042*\"thank\" + 0.017*\"twitter\" + 0.014*\"ill\" + 0.013*\"look\" + 0.012*\"feel\" + 0.011*\"free\"'),\n",
       " (7,\n",
       "  '0.014*\"job\" + 0.011*\"future\" + 0.010*\"world\" + 0.010*\"take\" + 0.008*\"elon\" + 0.008*\"technology\" + 0.008*\"replace\" + 0.007*\"tech\" + 0.007*\"potential\" + 0.007*\"musk\"'),\n",
       " (8,\n",
       "  '0.018*\"new\" + 0.016*\"create\" + 0.015*\"content\" + 0.014*\"video\" + 0.014*\"article\" + 0.013*\"check\" + 0.012*\"late\" + 0.012*\"read\" + 0.010*\"tool\" + 0.010*\"medium\"'),\n",
       " (9,\n",
       "  '0.136*\"chat\" + 0.023*\"app\" + 0.019*\"bot\" + 0.017*\"user\" + 0.016*\"open\" + 0.013*\"access\" + 0.013*\"internet\" + 0.009*\"build\" + 0.008*\"source\" + 0.008*\"available\"')]"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Inspect topics\n",
    "tweet_topic_LDA.print_topics()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "While not all topics can be clearly identified, the results of this LDA are considered acceptable."
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### LDA Topic Labelling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "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>tweet_id</th>\n",
       "      <th>tweet_content_hashtags</th>\n",
       "      <th>tweet_content_clean</th>\n",
       "      <th>tweet_content_tokens_no_verbs</th>\n",
       "      <th>tweet_content_tokens</th>\n",
       "      <th>tweet_topic</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1644446066748854281</td>\n",
       "      <td>[teaching]</td>\n",
       "      <td>whats happening it asks and i reply that chat ...</td>\n",
       "      <td>[video, game, class, together, reply, kind, en...</td>\n",
       "      <td>[video, game, class, together, reply, kind, en...</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1644442136664764444</td>\n",
       "      <td>[]</td>\n",
       "      <td>one in the depot is approximately mb in size t...</td>\n",
       "      <td>[mb, conversion, current, mib, depot, size, am...</td>\n",
       "      <td>[mb, conversion, current, mib, depot, size, am...</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1644437753721942044</td>\n",
       "      <td>[]</td>\n",
       "      <td>ive converted to chatgpt</td>\n",
       "      <td>[]</td>\n",
       "      <td>[]</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              tweet_id tweet_content_hashtags  \\\n",
       "0  1644446066748854281             [teaching]   \n",
       "1  1644442136664764444                     []   \n",
       "2  1644437753721942044                     []   \n",
       "\n",
       "                                 tweet_content_clean  \\\n",
       "0  whats happening it asks and i reply that chat ...   \n",
       "1  one in the depot is approximately mb in size t...   \n",
       "2                           ive converted to chatgpt   \n",
       "\n",
       "                       tweet_content_tokens_no_verbs  \\\n",
       "0  [video, game, class, together, reply, kind, en...   \n",
       "1  [mb, conversion, current, mib, depot, size, am...   \n",
       "2                                                 []   \n",
       "\n",
       "                                tweet_content_tokens tweet_topic  \n",
       "0  [video, game, class, together, reply, kind, en...              \n",
       "1  [mb, conversion, current, mib, depot, size, am...              \n",
       "2                                                 []              "
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Inspect dataframe\n",
    "tweets_prepro_df.head(3)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Initially, the LDA model chosen was the one resulting in 8 topics based on tokens including verbs that recorded the highest coherence score: 0.528. However, the topics were hard to identify based on the clusters resulted, thus the LDA model recording the second best coherence score was chosen. \n",
    "\n",
    "The output of the latter provides an easier way of idetifing and labelling the topics, however even these results are not considered to be completly cohesive. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Loading chosen LDA model : k = 12, alpha = asymmetric, beta = auto, no_iterations=100, coherence score = 0.527299\n",
    "tweet_topic_LDA = models.ldamodel.LdaModel.load('Tweets_LDA/LDA_Base_Tuning_Verbs/tweetsLDA12including_verbs')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[(0,\n",
       "  '0.034*\"student\" + 0.026*\"write\" + 0.017*\"school\" + 0.017*\"essay\" + 0.015*\"pass\" + 0.015*\"education\" + 0.014*\"exam\" + 0.014*\"teacher\" + 0.014*\"paper\" + 0.013*\"teach\"'),\n",
       " (1,\n",
       "  '0.040*\"ask\" + 0.030*\"answer\" + 0.027*\"question\" + 0.021*\"code\" + 0.013*\"make\" + 0.010*\"problem\" + 0.009*\"wrong\" + 0.008*\"write\" + 0.008*\"need\" + 0.007*\"work\"'),\n",
       " (2,\n",
       "  '0.053*\"write\" + 0.019*\"make\" + 0.015*\"prompt\" + 0.015*\"thread\" + 0.015*\"save\" + 0.015*\"help\" + 0.015*\"generate\" + 0.015*\"post\" + 0.015*\"ask\" + 0.013*\"work\"'),\n",
       " (3,\n",
       "  '0.018*\"chat\" + 0.016*\"make\" + 0.016*\"people\" + 0.016*\"time\" + 0.012*\"ask\" + 0.012*\"talk\" + 0.011*\"year\" + 0.011*\"write\" + 0.011*\"day\" + 0.010*\"come\"'),\n",
       " (4,\n",
       "  '0.028*\"model\" + 0.023*\"language\" + 0.016*\"human\" + 0.015*\"tool\" + 0.011*\"data\" + 0.010*\"learn\" + 0.010*\"train\" + 0.009*\"improve\" + 0.009*\"text\" + 0.008*\"base\"'),\n",
       " (5,\n",
       "  '0.061*\"google\" + 0.035*\"search\" + 0.033*\"microsoft\" + 0.026*\"bing\" + 0.025*\"openai\" + 0.021*\"new\" + 0.018*\"bard\" + 0.017*\"launch\" + 0.015*\"power\" + 0.013*\"engine\"'),\n",
       " (6,\n",
       "  '0.087*\"tweet\" + 0.070*\"response\" + 0.059*\"limit\" + 0.053*\"character\" + 0.042*\"thank\" + 0.017*\"twitter\" + 0.014*\"ill\" + 0.013*\"look\" + 0.012*\"feel\" + 0.011*\"free\"'),\n",
       " (7,\n",
       "  '0.014*\"job\" + 0.011*\"future\" + 0.010*\"world\" + 0.010*\"take\" + 0.008*\"elon\" + 0.008*\"technology\" + 0.008*\"replace\" + 0.007*\"tech\" + 0.007*\"potential\" + 0.007*\"musk\"'),\n",
       " (8,\n",
       "  '0.018*\"new\" + 0.016*\"create\" + 0.015*\"content\" + 0.014*\"video\" + 0.014*\"article\" + 0.013*\"check\" + 0.012*\"late\" + 0.012*\"read\" + 0.010*\"tool\" + 0.010*\"medium\"'),\n",
       " (9,\n",
       "  '0.136*\"chat\" + 0.023*\"app\" + 0.019*\"bot\" + 0.017*\"user\" + 0.016*\"open\" + 0.013*\"access\" + 0.013*\"internet\" + 0.009*\"build\" + 0.008*\"source\" + 0.008*\"available\"')]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tweet_topic_LDA.print_topics()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "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 clusters\n",
    "lda_dict = {0:'Learning & Education',\n",
    "            1:'Problem Solving',\n",
    "            2:'Problem Solving',\n",
    "            3:'Human-Bot Interaction',\n",
    "            4:'UnclearTopic',\n",
    "            5:'AI R&D Competition',\n",
    "            6:'UnclearTopic',\n",
    "            7:'Future of Jobs & Societal Impact',\n",
    "            8:'Content Generation',\n",
    "            9:'ChatGPT Features & Accesibility',\n",
    "            '-':'-'}\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 10 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 10 topics identified\n",
    "\n",
    "cols = [color for color in ['#ff8625','#6f93ff','#f56cff','#224f3c','#ea9999','#981c41','#05504c','#6a2292','#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 = tweet_topic_LDA.show_topics(formatted=False)\n",
    "\n",
    "fig, axes = plt.subplots(2, 5, figsize=(10,6), 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(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(\"TweetsTopics.jpg\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Defining a classifier function to use for labelling each user with an industry \n",
    "def classifier(texts,model,topics_dict):\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 descending 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",
    "  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",
    "  return texts_clean"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Classify the users descriptions into labels resulted from LDA model\n",
    "tweets_prepro_df['tweet_topic']= classifier(tweets_prepro_df['tweet_content_tokens'],tweet_topic_LDA,lda_dict)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "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>tweet_id</th>\n",
       "      <th>tweet_content_hashtags</th>\n",
       "      <th>tweet_content_clean</th>\n",
       "      <th>tweet_content_tokens_no_verbs</th>\n",
       "      <th>tweet_content_tokens</th>\n",
       "      <th>tweet_topic</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1644446066748854281</td>\n",
       "      <td>[teaching]</td>\n",
       "      <td>whats happening it asks and i reply that chat ...</td>\n",
       "      <td>[world, video, class, alternative, fall, reply...</td>\n",
       "      <td>[world, video, class, alternative, fall, reply...</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1644442136664764444</td>\n",
       "      <td>[]</td>\n",
       "      <td>one in the depot is approximately mb in size t...</td>\n",
       "      <td>[current, mib, mb, depot, correct, conversion,...</td>\n",
       "      <td>[current, mib, mb, depot, correct, conversion,...</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1644437753721942044</td>\n",
       "      <td>[]</td>\n",
       "      <td>ive converted to chatgpt</td>\n",
       "      <td>[]</td>\n",
       "      <td>[]</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1644433541827280896</td>\n",
       "      <td>[chatgpt]</td>\n",
       "      <td>asked if someone has a bmi of  are they obese ...</td>\n",
       "      <td>[health, doc, account, replace, risk, age, rep...</td>\n",
       "      <td>[health, doc, account, replace, risk, age, rep...</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1644429028307406849</td>\n",
       "      <td>[ChatGPT, AI, Hardware, blog]</td>\n",
       "      <td>how does chatgpt train its ai using advanced h...</td>\n",
       "      <td>[blog, post, advanced, train, secret, explore,...</td>\n",
       "      <td>[blog, post, advanced, train, secret, explore,...</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              tweet_id         tweet_content_hashtags  \\\n",
       "0  1644446066748854281                     [teaching]   \n",
       "1  1644442136664764444                             []   \n",
       "2  1644437753721942044                             []   \n",
       "3  1644433541827280896                      [chatgpt]   \n",
       "4  1644429028307406849  [ChatGPT, AI, Hardware, blog]   \n",
       "\n",
       "                                 tweet_content_clean  \\\n",
       "0  whats happening it asks and i reply that chat ...   \n",
       "1  one in the depot is approximately mb in size t...   \n",
       "2                           ive converted to chatgpt   \n",
       "3  asked if someone has a bmi of  are they obese ...   \n",
       "4  how does chatgpt train its ai using advanced h...   \n",
       "\n",
       "                       tweet_content_tokens_no_verbs  \\\n",
       "0  [world, video, class, alternative, fall, reply...   \n",
       "1  [current, mib, mb, depot, correct, conversion,...   \n",
       "2                                                 []   \n",
       "3  [health, doc, account, replace, risk, age, rep...   \n",
       "4  [blog, post, advanced, train, secret, explore,...   \n",
       "\n",
       "                                tweet_content_tokens tweet_topic  \n",
       "0  [world, video, class, alternative, fall, reply...         NaN  \n",
       "1  [current, mib, mb, depot, correct, conversion,...         NaN  \n",
       "2                                                 []         NaN  \n",
       "3  [health, doc, account, replace, risk, age, rep...         NaN  \n",
       "4  [blog, post, advanced, train, secret, explore,...         NaN  "
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tweets_prepro_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "#TODO: if new topics need to be assigned remove comment below\n",
    "#tweets_prepro_df.to_csv('Tweets_TopicLabelled.csv',index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1640046, 12)"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sentiment_df= pd.read_csv(\"data_files/Tweets_SentimentLabelled_PreLDA.csv\")\n",
    "sentiment_df['tweet_id']=sentiment_df['tweet_id'].astype(str)\n",
    "sentiment_df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1640046, 6)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "topics_df= pd.read_csv(\"data_files/Tweets_TopicLabelled.csv\")\n",
    "topics_df['tweet_id']=topics_df['tweet_id'].astype(str)\n",
    "topics_df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe thead th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>tweet_id</th>\n",
       "      <th>tweet_topic</th>\n",
       "      <th>sentiment</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1644446066748854281</td>\n",
       "      <td>Human-Bot Interaction</td>\n",
       "      <td>Positive</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1644442136664764444</td>\n",
       "      <td>Problem Solving</td>\n",
       "      <td>Neutral</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1644437753721942044</td>\n",
       "      <td>Education</td>\n",
       "      <td>Neutral</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1644433541827280896</td>\n",
       "      <td>Human-Bot Interaction</td>\n",
       "      <td>Negative</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1644429028307406849</td>\n",
       "      <td>Content Generation</td>\n",
       "      <td>Positive</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              tweet_id            tweet_topic sentiment\n",
       "0  1644446066748854281  Human-Bot Interaction  Positive\n",
       "1  1644442136664764444        Problem Solving   Neutral\n",
       "2  1644437753721942044              Education   Neutral\n",
       "3  1644433541827280896  Human-Bot Interaction  Negative\n",
       "4  1644429028307406849     Content Generation  Positive"
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "full_df= sentiment_df.merge(topics_df,on=\"tweet_id\",how=\"left\")[['tweet_id','tweet_topic','sentiment']]\n",
    "full_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#full_df.to_csv(\"Tweets_TOPIC_SENTIMENT.csv\",index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>tweet_id</th>\n",
       "      <th>tweet_topic</th>\n",
       "      <th>sentiment</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1644446066748854281</td>\n",
       "      <td>Human-Bot Interaction</td>\n",
       "      <td>Positive</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1644442136664764444</td>\n",
       "      <td>Problem Solving</td>\n",
       "      <td>Neutral</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1644437753721942044</td>\n",
       "      <td>Education</td>\n",
       "      <td>Neutral</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1644433541827280896</td>\n",
       "      <td>Human-Bot Interaction</td>\n",
       "      <td>Negative</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1644429028307406849</td>\n",
       "      <td>Content Generation</td>\n",
       "      <td>Positive</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1640041</th>\n",
       "      <td>1632455286211371009</td>\n",
       "      <td>AI R&amp;D Competition</td>\n",
       "      <td>Neutral</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1640042</th>\n",
       "      <td>1632455296441360386</td>\n",
       "      <td>UnclearTopic</td>\n",
       "      <td>Positive</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1640043</th>\n",
       "      <td>1632455301701189632</td>\n",
       "      <td>Content Generation</td>\n",
       "      <td>Positive</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1640044</th>\n",
       "      <td>1632455315005509632</td>\n",
       "      <td>UnclearTopic</td>\n",
       "      <td>Positive</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1640045</th>\n",
       "      <td>1632455352922021890</td>\n",
       "      <td>UnclearTopic</td>\n",
       "      <td>Positive</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1640046 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                    tweet_id            tweet_topic sentiment\n",
       "0        1644446066748854281  Human-Bot Interaction  Positive\n",
       "1        1644442136664764444        Problem Solving   Neutral\n",
       "2        1644437753721942044              Education   Neutral\n",
       "3        1644433541827280896  Human-Bot Interaction  Negative\n",
       "4        1644429028307406849     Content Generation  Positive\n",
       "...                      ...                    ...       ...\n",
       "1640041  1632455286211371009     AI R&D Competition   Neutral\n",
       "1640042  1632455296441360386           UnclearTopic  Positive\n",
       "1640043  1632455301701189632     Content Generation  Positive\n",
       "1640044  1632455315005509632           UnclearTopic  Positive\n",
       "1640045  1632455352922021890           UnclearTopic  Positive\n",
       "\n",
       "[1640046 rows x 3 columns]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tweets=pd.read_csv(\"data_files/Tweets_TOPIC_SENTIMENT.csv\")\n",
    "tweets['tweet_id']=tweets['tweet_id'].astype(str)\n",
    "tweets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\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>tweet_id</th>\n",
       "      <th>sentiment</th>\n",
       "      <th>tweet_topic</th>\n",
       "      <th>tweet_date</th>\n",
       "      <th>tweet_content</th>\n",
       "      <th>tweet_replycount</th>\n",
       "      <th>tweet_retweetcount</th>\n",
       "      <th>tweet_likecount</th>\n",
       "      <th>usr_verified</th>\n",
       "      <th>usr_follower_count</th>\n",
       "      <th>usr_location</th>\n",
       "      <th>usr_userid</th>\n",
       "      <th>usr_inreply_id</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1644446066748854281</td>\n",
       "      <td>Positive</td>\n",
       "      <td>UnclearTopic</td>\n",
       "      <td>2023-04-07 21:04:29+00:00</td>\n",
       "      <td>What's happening, it asks, and I reply that ch...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>False</td>\n",
       "      <td>9</td>\n",
       "      <td>Sacramento, CA</td>\n",
       "      <td>1476626055993561110</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1644442136664764444</td>\n",
       "      <td>Neutral</td>\n",
       "      <td>Code Assistance</td>\n",
       "      <td>2023-04-07 20:48:52+00:00</td>\n",
       "      <td>...one in the depot is approximately 17,42 MB ...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "      <td>55</td>\n",
       "      <td>Liberty City</td>\n",
       "      <td>1533967598802575361</td>\n",
       "      <td>1533967598802575360</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1644437753721942044</td>\n",
       "      <td>Neutral</td>\n",
       "      <td>UnclearTopic</td>\n",
       "      <td>2023-04-07 20:31:27+00:00</td>\n",
       "      <td>I’ve converted to chatgpt</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>False</td>\n",
       "      <td>357</td>\n",
       "      <td>the u.s., sadly</td>\n",
       "      <td>719368782</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              tweet_id sentiment      tweet_topic                 tweet_date  \\\n",
       "0  1644446066748854281  Positive     UnclearTopic  2023-04-07 21:04:29+00:00   \n",
       "1  1644442136664764444   Neutral  Code Assistance  2023-04-07 20:48:52+00:00   \n",
       "2  1644437753721942044   Neutral     UnclearTopic  2023-04-07 20:31:27+00:00   \n",
       "\n",
       "                                       tweet_content  tweet_replycount  \\\n",
       "0  What's happening, it asks, and I reply that ch...                 0   \n",
       "1  ...one in the depot is approximately 17,42 MB ...                 1   \n",
       "2                          I’ve converted to chatgpt                 0   \n",
       "\n",
       "   tweet_retweetcount  tweet_likecount  usr_verified  usr_follower_count  \\\n",
       "0                   0                0         False                   9   \n",
       "1                   0                1         False                  55   \n",
       "2                   0                0         False                 357   \n",
       "\n",
       "      usr_location           usr_userid       usr_inreply_id  \n",
       "0   Sacramento, CA  1476626055993561110                  nan  \n",
       "1     Liberty City  1533967598802575361  1533967598802575360  \n",
       "2  the u.s., sadly            719368782                  nan  "
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "all_cols_data= pd.read_csv('data_files/tweets_data_condensed_FINAL.csv')\n",
    "all_cols_data['tweet_id']=all_cols_data['tweet_id'].astype(str)\n",
    "all_cols_data['usr_inreply_id']=all_cols_data['usr_inreply_id'].apply(lambda x: '%.0f' % x)\n",
    "all_cols_data['usr_inreply_id'] =all_cols_data['usr_inreply_id'].astype(str)\n",
    "all_cols_data.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "all_cols_data.drop(columns=['sentiment','tweet_topic'],inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "#all_cols_data.merge(tweets,on='tweet_id',how='left').to_csv(\"tweets_data_condensed_FINAL.csv\",index=False)"
   ]
  }
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