Twitter Analytics.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 1. Twitter Data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### The first step is to import the libraries required for twitter scraping and data manipulation "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[nltk_data] Downloading package punkt to /Users/vasusingh/nltk_data...\n",
      "[nltk_data]   Package punkt is already up-to-date!\n",
      "[nltk_data] Downloading package stopwords to\n",
      "[nltk_data]     /Users/vasusingh/nltk_data...\n",
      "[nltk_data]   Package stopwords is already up-to-date!\n"
     ]
    }
   ],
   "source": [
    "import json\n",
    "import time\n",
    "import twitter\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "pd.set_option('display.max_columns',10)\n",
    "import matplotlib.pyplot as plt\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "import nltk\n",
    "nltk.download('punkt')\n",
    "nltk.download('stopwords')\n",
    "import re\n",
    "from nltk.tokenize import word_tokenize\n",
    "from string import punctuation \n",
    "from nltk.corpus import stopwords\n",
    "import textblob"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Setting up the authorization : Twitter APIs handle enormous amounts of data. The way this data is secured for developers and users alike is through authentication."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# To set up your Twitter app and get your secret key, refer to the README\n",
    "# Enter your credentials below\n",
    "\n",
    "consumer_key = ''\n",
    "consumer_secret = ''\n",
    "access_key= ''\n",
    "access_secret = ''\n",
    "\n",
    "# Authorization\n",
    "twitter_api = twitter.Api(consumer_key = consumer_key,\n",
    "                              consumer_secret = consumer_secret,\n",
    "                              access_token_key = access_key,\n",
    "                              access_token_secret= access_secret,\n",
    "                              tweet_mode='extended') # extended mode for scraping the full text from tweets"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Scraping Tweets with a search keyword"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Enter Your Search Term : US Elections\n",
      "How many Tweets Do you want ? : 1300\n",
      "Fetching Tweets : >>\n",
      "Done -->\n"
     ]
    }
   ],
   "source": [
    "def getmultipleData(search_keyword,no): # search_keyword -> topic to search tweets, no -> number of tweets\n",
    "    \n",
    "    count = 20 # count -> number of tweets to scrape in one request.Try to keep less to avoid duplicates.\n",
    "    lowest_id = 0\n",
    "    \n",
    "    with open('Tweets'+ search_term + str('.json'),'a') as outfile: # opening json file to save the data\n",
    "        \n",
    "        time.sleep(5)\n",
    "        results = twitter_api.GetSearch(term = search_keyword, count = count,lang = 'en')\n",
    "        \n",
    "        for tweet in results:\n",
    "            tweet = str(tweet).replace('\\n',' ').replace('\\r',' ') # remove new lines\n",
    "            tweet = (json.loads(tweet))\n",
    "            lowest_id = tweet['id'] # keep track of lowest tweet on the current page\n",
    "            json.dump(tweet,outfile)\n",
    "            outfile.write('\\n') #print tweets on new lines\n",
    "            \n",
    "        for i in range(0,(no-count)//count):\n",
    "            \n",
    "            time.sleep(5)\n",
    "            results = twitter_api.GetSearch(term = search_keyword, count = count, max_id = lowest_id-1,lang = 'en')\n",
    "            \n",
    "            for tweet in results:\n",
    "                tweet = str(tweet).replace('\\n',' ').replace('\\r',' ') # remove new lines\n",
    "                tweet = (json.loads(tweet))\n",
    "                lowest_id = tweet['id'] # keep track of lowest tweet on the current page\n",
    "                json.dump(tweet,outfile)\n",
    "                outfile.write('\\n') #print tweets on new lines\n",
    "\n",
    "                \n",
    "# input for keyword and number of tweets                                \n",
    "search_term = input('Enter Your Search Term : ')\n",
    "no_of_tweets = int(input('How many Tweets Do you want ? : '))\n",
    "\n",
    "print('Fetching Tweets : >>')\n",
    "getmultipleData(search_term,no_of_tweets)\n",
    "print('Done -->')\n",
    "\n",
    "\n",
    "data = [json.loads(line) for line in open('Tweets'+ search_term + str('.json'), 'r')]\n",
    "\n",
    "list_tweets = []\n",
    "\n",
    "for i in range(len(data)):\n",
    "    try: \n",
    "        list_tweets.append(data[i]['retweeted_status']['full_text'])    \n",
    "    except:\n",
    "        list_tweets.append(data[i]['full_text'])   \n",
    "        \n",
    "list_tweets = set(list_tweets) # Removing any duplicate/retweeted tweets\n",
    "\n",
    "# final dataframe consisting of scraped tweets\n",
    "final_df = pd.DataFrame(data = list_tweets,columns=['Tweet'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "## To get the data which is already scraped\n",
    "search_term = 'US Elections'\n",
    "data = [json.loads(line) for line in open('Tweets'+ search_term + str('.json'), 'r')]\n",
    "\n",
    "list_tweets = []\n",
    "\n",
    "for i in range(len(data)):\n",
    "    try: \n",
    "        list_tweets.append(data[i]['retweeted_status']['full_text'])    \n",
    "    except:\n",
    "        list_tweets.append(data[i]['full_text'])   \n",
    "        \n",
    "list_tweets = set(list_tweets) # Removing any duplicate/retweeted tweets\n",
    "\n",
    "# final dataframe consisting of scraped tweets\n",
    "final_df = pd.DataFrame(data = list_tweets,columns=['Tweet'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Additional : It is also possible to scrape the tweets from users of choice. For this analysis we will stick to our previously scraped data\n",
    "  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "## Getting Twitter Data from a Particular User\n",
    "\n",
    "twitter_handles = ['PlattsOil','IEA','markets','IHSMarkit','EIAgov','WorldOil','OGJOnline','OPECSecretariat','OPIS',\n",
    "                   'ftenergy','ETEnergyNews','ESAIEnergy','RefinitivEnergy','BloombergNRG']\n",
    "\n",
    "\n",
    "twitter_people = ['realDonaldTrump','IEABirol','JZarif','SecPompeo','aeberman12']\n",
    "    \n",
    "def getuserdata(user_name):\n",
    "    \n",
    "    results = twitter_api.GetUserTimeline(screen_name = user_name,count = 600,include_rts = False,exclude_replies=True)\n",
    "    l =  [{'Text':status.full_text,'Fav-Count':status.favorite_count,'Retweet-Count':status.retweet_count} for status in results]\n",
    "    user_tweets = []\n",
    "    for i in range(len(results)):\n",
    "        \n",
    "        user_tweets.append(l[i]['Text'])\n",
    "    \n",
    "    return user_tweets\n",
    "\n",
    "choice = int(input('Enter Your Choice (1(Twitter Accounts) or 2(Important People Accounts)) : '))\n",
    "print('Fetching Twitter Data -->')\n",
    "\n",
    "if choice == 1:\n",
    "    \n",
    "    for i in twitter_handles:\n",
    "    \n",
    "        \n",
    "        user_tweets = getuserdata(i)\n",
    "        spec_df = pd.DataFrame(data = user_tweets,columns=['Tweet'])\n",
    "        # Saving The Tweets\n",
    "        writer = pd.ExcelWriter('Tweets_ '+ i +str('.xlsx'))\n",
    "        # write dataframe to excel\n",
    "        spec_df.to_excel(writer)\n",
    "        # save the excel\n",
    "        writer.save()\n",
    "    \n",
    "elif choice == 2:\n",
    "    \n",
    "    for i in twitter_people:\n",
    "    \n",
    "        \n",
    "        user_tweets = getuserdata(i)\n",
    "        spec_df = pd.DataFrame(data = user_tweets,columns=['Tweet'])\n",
    "        # Saving The Tweets\n",
    "        writer = pd.ExcelWriter('Tweets_ '+ i +str('.xlsx'))\n",
    "        # write dataframe to excel\n",
    "        spec_df.to_excel(writer)\n",
    "        # save the excel\n",
    "        writer.save()\n",
    "    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Scraped Tweets:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
       "    .dataframe thead th {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Tweet</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>All the living past presidents of @APSAtweets ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>@criha @realDonaldTrump The Democrats lost 9 p...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>@honilatte interfered with the US last electio...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Americans have sacrificed too much due to the ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>@SMcTavishESQ @paulsperry_ Being\"in the majori...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>500</th>\n",
       "      <td>Harris Calls Trump’s Refusal to Condemn Racism...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>501</th>\n",
       "      <td>EP. 11 | The Dark Side of Politics/Elections\\n...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>502</th>\n",
       "      <td>@MariaTCardona @HillaryClinton @Elreynetwork \"...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>503</th>\n",
       "      <td>Pence, Harris asked about the environment and ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>504</th>\n",
       "      <td>4 takeaways from the vice presidential debate ...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>505 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                 Tweet\n",
       "0    All the living past presidents of @APSAtweets ...\n",
       "1    @criha @realDonaldTrump The Democrats lost 9 p...\n",
       "2    @honilatte interfered with the US last electio...\n",
       "3    Americans have sacrificed too much due to the ...\n",
       "4    @SMcTavishESQ @paulsperry_ Being\"in the majori...\n",
       "..                                                 ...\n",
       "500  Harris Calls Trump’s Refusal to Condemn Racism...\n",
       "501  EP. 11 | The Dark Side of Politics/Elections\\n...\n",
       "502  @MariaTCardona @HillaryClinton @Elreynetwork \"...\n",
       "503  Pence, Harris asked about the environment and ...\n",
       "504  4 takeaways from the vice presidential debate ...\n",
       "\n",
       "[505 rows x 1 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final_df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 2. Building WordCloud"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Clean Tweets and Visualize frequent words"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/vasusingh/opt/anaconda3/envs/twitter-analytics/lib/python3.8/site-packages/ipykernel/ipkernel.py:287: DeprecationWarning: `should_run_async` will not call `transform_cell` automatically in the future. Please pass the result to `transformed_cell` argument and any exception that happen during thetransform in `preprocessing_exc_tuple` in IPython 7.17 and above.\n",
      "  and should_run_async(code)\n",
      "<>:19: DeprecationWarning: invalid escape sequence \\.\n",
      "<>:20: DeprecationWarning: invalid escape sequence \\s\n",
      "<>:19: DeprecationWarning: invalid escape sequence \\.\n",
      "<>:20: DeprecationWarning: invalid escape sequence \\s\n",
      "<ipython-input-18-470b88337179>:19: DeprecationWarning: invalid escape sequence \\.\n",
      "  tweet = re.sub('((www\\.[^\\s]+)|(https?://[^\\s]+))', 'URL', tweet) # remove URLs\n",
      "<ipython-input-18-470b88337179>:20: DeprecationWarning: invalid escape sequence \\s\n",
      "  tweet = re.sub('@[^\\s]+', 'AT_USER', tweet) # remove usernames\n"
     ]
    }
   ],
   "source": [
    "class PreProcessTweets:\n",
    "    def __init__(self):\n",
    "        self._stopwords = set(stopwords.words('english') + list(punctuation) + ['AT_USER','URL','rt','amp']) # to remove the stopwords\n",
    "\n",
    "    def processTweets(self, list_of_tweets):\n",
    "        processedTweets=[]\n",
    "        for tweet in list_of_tweets:\n",
    "            processedTweets.append((self._processTweet(tweet)))\n",
    "            \n",
    "        l = []\n",
    "        for items in processedTweets:\n",
    "            l.append(' '.join(items))\n",
    "            \n",
    "        df = pd.DataFrame(l,columns=['ProcessedTweets'])\n",
    "        return df\n",
    "    \n",
    "    def _processTweet(self, tweet):\n",
    "        tweet = tweet.lower() # convert text to lower-case\n",
    "        tweet = re.sub('((www\\.[^\\s]+)|(https?://[^\\s]+))', 'URL', tweet) # remove URLs\n",
    "        tweet = re.sub('@[^\\s]+', 'AT_USER', tweet) # remove usernames\n",
    "        tweet = re.sub(r'#([^\\s]+)', '', tweet) # remove the # in #hashtag\n",
    "        tweet = word_tokenize(tweet) # remove repeated characters (helloooooooo into hello)\n",
    "        return [word for word in tweet if word not in self._stopwords]\n",
    "\n",
    "## Pre-Processing Our Tweets\n",
    "tweetProcessor = PreProcessTweets()\n",
    "cleaned_tweets = tweetProcessor.processTweets(final_df.Tweet)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/vasusingh/opt/anaconda3/envs/twitter-analytics/lib/python3.8/site-packages/ipykernel/ipkernel.py:287: DeprecationWarning: `should_run_async` will not call `transform_cell` automatically in the future. Please pass the result to `transformed_cell` argument and any exception that happen during thetransform in `preprocessing_exc_tuple` in IPython 7.17 and above.\n",
      "  and should_run_async(code)\n"
     ]
    },
    {
     "data": {
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe thead th {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ProcessedTweets</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>living past presidents op-ed “ outcome going k...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>democrats lost 9 presidential elections row ab...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>interfered us last elections also one blaming ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>americans sacrificed much due 'incompetence ad...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>'' majority '' met winner '' many past preside...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>500</th>\n",
       "      <td>harris calls trump ’ refusal condemn racism ‘ ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>501</th>\n",
       "      <td>ep 11 dark side politics/elections christian e...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>502</th>\n",
       "      <td>`` clinton ’ approval plan concerning us presi...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>503</th>\n",
       "      <td>pence harris asked environment climate change ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>504</th>\n",
       "      <td>4 takeaways vice presidential debate</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>505 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                       ProcessedTweets\n",
       "0    living past presidents op-ed “ outcome going k...\n",
       "1    democrats lost 9 presidential elections row ab...\n",
       "2    interfered us last elections also one blaming ...\n",
       "3    americans sacrificed much due 'incompetence ad...\n",
       "4    '' majority '' met winner '' many past preside...\n",
       "..                                                 ...\n",
       "500  harris calls trump ’ refusal condemn racism ‘ ...\n",
       "501  ep 11 dark side politics/elections christian e...\n",
       "502  `` clinton ’ approval plan concerning us presi...\n",
       "503  pence harris asked environment climate change ...\n",
       "504               4 takeaways vice presidential debate\n",
       "\n",
       "[505 rows x 1 columns]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cleaned_tweets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/vasusingh/opt/anaconda3/envs/twitter-analytics/lib/python3.8/site-packages/ipykernel/ipkernel.py:287: DeprecationWarning: `should_run_async` will not call `transform_cell` automatically in the future. Please pass the result to `transformed_cell` argument and any exception that happen during thetransform in `preprocessing_exc_tuple` in IPython 7.17 and above.\n",
      "  and should_run_async(code)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Building The WordCloud : >>\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "## Remember To clean The Tweets before for better results\n",
    "def buildwordcloud(df,search_term):\n",
    "    import wordcloud\n",
    "    comment_words = ' '\n",
    "    stopwords = set(wordcloud.STOPWORDS)# using stopwords included in wordcloud library\n",
    "    stopwords = list(stopwords) + ['AT_USER','URL','rt','amp',\"n't\"] + search_term.split() + ['election'] # adding our own stopwords\n",
    "    for val in df['ProcessedTweets']:\n",
    "        val = str(val)\n",
    "        tokens = val.split()\n",
    "        for i in range(len(tokens)):\n",
    "            tokens[i] = tokens[i].lower()\n",
    "        for word in tokens:\n",
    "            comment_words = comment_words + word + ' '\n",
    "    wordcloud = wordcloud.WordCloud(width=500,height=500,max_words = 100,background_color='white',stopwords=stopwords,min_font_size=10).generate(comment_words)\n",
    "\n",
    "    # plot the wordcloud image\n",
    "    plt.figure(figsize=(7,7),facecolor=None)\n",
    "    plt.imshow(wordcloud)\n",
    "    plt.axis('off')\n",
    "    plt.tight_layout(pad=0)\n",
    "    plt.show()\n",
    "\n",
    "print('Building The WordCloud : >>')\n",
    "buildwordcloud(cleaned_tweets,search_term)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[['all', 'the', 'living', 'past', 'presidents', 'of', 'apsatweets', 'in', 'this', 'op', 'ed', 'if', 'the', 'outcome', 'is', 'not', 'going', 'to', 'be', 'known', 'on', 'election', 'night', 'play', 'by', 'play', 'coverage', 'on', 'the', 'model', 'of', 'past', 'elections', 'is', 'unnecessary', 'and', 'in', 'fact', 'dangerously', 'uninformative', 'https', 'co', 'wz']]\n"
     ]
    }
   ],
   "source": [
    "# Tokenize\n",
    "import gensim\n",
    "\n",
    "def sent_to_words(sentences):\n",
    "    for sentence in sentences:\n",
    "        yield(gensim.utils.simple_preprocess(str(sentence),deacc=True))\n",
    "        \n",
    "data_words = list(sent_to_words(final_df['Tweet']))\n",
    "print(data_words[:1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['live past president apsatweet op ed outcome go know election night play play coverage model past election unnecessary fact dangerously uninformative https co', 'realdonaldtrump lose presidential election row say only way win again put ger back plantation come public housing amp welfare exactly']\n"
     ]
    }
   ],
   "source": [
    "# Lemmatization\n",
    "import spacy\n",
    "\n",
    "def lemmatization(texts, allowed_postags=['NOUN', 'ADJ', 'VERB', 'ADV']):\n",
    "    \"\"\"https://spacy.io/api/annotation\"\"\"\n",
    "    texts_out = []\n",
    "    for sent in texts:\n",
    "        doc = nlp(\" \".join(sent)) \n",
    "        texts_out.append(\" \".join([token.lemma_ if token.lemma_ not in ['-PRON-'] else '' for token in doc if token.pos_ in allowed_postags]))\n",
    "    return texts_out\n",
    "\n",
    "# Initialize spacy 'en' model, keeping only tagger component (for efficiency)\n",
    "\n",
    "# python -m spacy download en_core_web_sm\n",
    "# python -m spacy download en\n",
    "\n",
    "nlp = spacy.load('en', disable=['parser', 'ner'])\n",
    "\n",
    "# Do lemmatization keeping only Noun, Adjective, Verb, Adverb\n",
    "data_lemmatized = lemmatization(data_words, allowed_postags=['NOUN', 'ADJ', 'VERB', 'ADV'])\n",
    "\n",
    "print(data_lemmatized[:2])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.feature_extraction.text import CountVectorizer\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "from sklearn.decomposition import LatentDirichletAllocation as LDA,TruncatedSVD"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Word-Document Matrix\n",
    "\n",
    "vectorizer = CountVectorizer(analyzer='word',       \n",
    "                             min_df=10,                        # minimum number occurences of a word required\n",
    "                             stop_words='english',             # remove stop words\n",
    "                             lowercase=True,                   # convert all words to lowercase\n",
    "                             token_pattern='[a-zA-Z0-9]{3,}')  # num of characters > 3\n",
    "                            \n",
    "data_vectorized = vectorizer.fit_transform(data_lemmatized)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "LatentDirichletAllocation(batch_size=32, learning_method='online',\n",
      "                          n_components=15, random_state=100)\n"
     ]
    }
   ],
   "source": [
    "# Build LDA model with Sklearn\n",
    "\n",
    "lda_model = LDA(n_components=15,               # Number of topics\n",
    "                max_iter=10,                   # Max learning iterations\n",
    "                learning_method='online',   \n",
    "                random_state=100,              # Random state\n",
    "                batch_size=32)                # n docs in each learning iter\n",
    "                                            \n",
    "                                      \n",
    "lda_output = lda_model.fit_transform(data_vectorized)\n",
    "print(lda_model) # Model attributes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Log Likelihood:  -11413.406093676296\n",
      "Perplexity:  94.53650796395875\n"
     ]
    }
   ],
   "source": [
    "#Evaluate model quality\n",
    "\n",
    "# Log Likelyhood: Higher the better\n",
    "print(\"Log Likelihood: \", lda_model.score(data_vectorized))\n",
    "\n",
    "# Perplexity: Lower the better. Perplexity = exp((-1) * log-likelihood per word)\n",
    "print(\"Perplexity: \", lda_model.perplexity(data_vectorized))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "<link rel=\"stylesheet\" type=\"text/css\" href=\"https://cdn.rawgit.com/bmabey/pyLDAvis/files/ldavis.v1.0.0.css\">\n",
       "\n",
       "\n",
       "<div id=\"ldavis_el16161404853164538722255737590\"></div>\n",
       "<script type=\"text/javascript\">\n",
       "\n",
       "var ldavis_el16161404853164538722255737590_data = {\"mdsDat\": {\"x\": [114.34487915039062, 105.821044921875, -138.33447265625, -7.436697483062744, -207.55062866210938, 52.817649841308594, 168.01084899902344, -94.16960144042969, -122.84917449951172, -94.8942642211914, 24.927400588989258, -53.41346740722656, -12.255849838256836, -224.21795654296875, 36.15425491333008], \"y\": [114.30829620361328, -93.74962615966797, 95.32076263427734, 111.25969696044922, -105.47260284423828, 14.603764533996582, 6.806949615478516, -157.78955078125, -39.059417724609375, 195.40829467773438, -180.46487426757812, 26.832107543945312, -72.09320068359375, 28.070581436157227, 209.42758178710938], \"topics\": [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15], \"cluster\": [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], \"Freq\": [12.795397252555016, 11.398232460297395, 11.368646633151897, 9.65303091743046, 8.377288951817045, 7.9163149145846265, 7.6949025054021165, 6.39220556800507, 4.928441471717982, 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0.9080863319076949, 0.9231575166109957, 0.22376155818881732, 0.7634217867618474, 0.2992108304269392, 0.5984216608538784, 0.503748470750641, 0.41979039229220083, 0.9544210625804637, 0.9540284731394055, 0.8033113530457556, 0.15448295250879915, 0.11793252988367459, 0.65518072157597, 0.13103614431519398, 0.07862168658911639, 0.9406631743402478, 0.782631070605613, 0.19565776765140325, 0.5790867698939064, 0.37227006636036836, 0.9734327182892781, 0.9596816598294453, 0.7368290038456721, 0.12280483397427869, 0.061402416987139344, 0.7149457381078205, 0.2599802684028438, 0.9422342851444283, 0.9218925956267046, 0.9582734841601721], \"Term\": [\"administration\", \"american\", \"american\", \"american\", \"amp\", \"ask\", \"ask\", \"ballot\", \"biden\", \"biden\", \"candidate\", \"care\", \"care\", \"care\", \"change\", \"change\", \"change\", \"clash\", \"close\", \"come\", \"country\", \"country\", \"covid\", \"covid\", \"day\", \"debate\", \"debate\", \"debate\", \"debate\", \"election\", \"election\", \"election\", \"election\", \"election\", \"election\", \"election\", \"election\", \"election\", \"election\", \"end\", \"fact\", \"fly\", \"free\", \"free\", \"good\", \"great\", \"help\", \"help\", \"help\", \"history\", \"https\", \"https\", \"https\", \"important\", \"important\", \"interfere\", \"just\", \"just\", \"justice\", \"justice\", \"know\", \"leader\", \"let\", \"let\", \"let\", \"live\", \"live\", \"long\", \"long\", \"lose\", \"mail\", \"make\", \"man\", \"need\", \"need\", \"new\", \"pandemic\", \"pandemic\", \"party\", \"pence\", \"penny\", \"people\", \"people\", \"plan\", \"plan\", \"plan\", \"point\", \"policy\", \"politic\", \"political\", \"poll\", \"president\", \"president\", \"presidential\", \"presidential\", \"public\", \"public\", \"public\", \"question\", \"ready\", \"realdonaldtrump\", \"realdonaldtrump\", \"really\", \"response\", \"response\", \"result\", \"result\", \"right\", \"rule\", \"rule\", \"russian\", \"say\", \"say\", \"say\", \"say\", \"send\", \"send\", \"speak\", \"speak\", \"state\", \"stop\", \"stop\", \"support\", \"talk\", \"tell\", \"tell\", \"thing\", \"think\", \"time\", \"time\", \"time\", \"today\", \"track\", \"trump\", \"trump\", \"try\", \"try\", \"upcoming\", \"upcoming\", \"update\", \"use\", \"vice\", \"vice\", \"vote\", \"vote\", \"vote\", \"vote\", \"voter\", \"voting\", \"voting\", \"vpdebate\", \"vpdebate\", \"want\", \"watch\", \"way\", \"way\", \"way\", \"win\", \"win\", \"work\", \"world\", \"year\"]}, \"R\": 30, \"lambda.step\": 0.01, \"plot.opts\": {\"xlab\": \"PC1\", \"ylab\": \"PC2\"}, \"topic.order\": [9, 8, 5, 7, 15, 6, 2, 3, 4, 1, 13, 11, 10, 12, 14]};\n",
       "\n",
       "function LDAvis_load_lib(url, callback){\n",
       "  var s = document.createElement('script');\n",
       "  s.src = url;\n",
       "  s.async = true;\n",
       "  s.onreadystatechange = s.onload = callback;\n",
       "  s.onerror = function(){console.warn(\"failed to load library \" + url);};\n",
       "  document.getElementsByTagName(\"head\")[0].appendChild(s);\n",
       "}\n",
       "\n",
       "if(typeof(LDAvis) !== \"undefined\"){\n",
       "   // already loaded: just create the visualization\n",
       "   !function(LDAvis){\n",
       "       new LDAvis(\"#\" + \"ldavis_el16161404853164538722255737590\", ldavis_el16161404853164538722255737590_data);\n",
       "   }(LDAvis);\n",
       "}else if(typeof define === \"function\" && define.amd){\n",
       "   // require.js is available: use it to load d3/LDAvis\n",
       "   require.config({paths: {d3: \"https://cdnjs.cloudflare.com/ajax/libs/d3/3.5.5/d3.min\"}});\n",
       "   require([\"d3\"], function(d3){\n",
       "      window.d3 = d3;\n",
       "      LDAvis_load_lib(\"https://cdn.rawgit.com/bmabey/pyLDAvis/files/ldavis.v1.0.0.js\", function(){\n",
       "        new LDAvis(\"#\" + \"ldavis_el16161404853164538722255737590\", ldavis_el16161404853164538722255737590_data);\n",
       "      });\n",
       "    });\n",
       "}else{\n",
       "    // require.js not available: dynamically load d3 & LDAvis\n",
       "    LDAvis_load_lib(\"https://cdnjs.cloudflare.com/ajax/libs/d3/3.5.5/d3.min.js\", function(){\n",
       "         LDAvis_load_lib(\"https://cdn.rawgit.com/bmabey/pyLDAvis/files/ldavis.v1.0.0.js\", function(){\n",
       "                 new LDAvis(\"#\" + \"ldavis_el16161404853164538722255737590\", ldavis_el16161404853164538722255737590_data);\n",
       "            })\n",
       "         });\n",
       "}\n",
       "</script>"
      ],
      "text/plain": [
       "PreparedData(topic_coordinates=                x           y  topics  cluster       Freq\n",
       "topic                                                    \n",
       "8      114.344879  114.308296       1        1  12.795397\n",
       "7      105.821045  -93.749626       2        1  11.398232\n",
       "4     -138.334473   95.320763       3        1  11.368647\n",
       "6       -7.436697  111.259697       4        1   9.653031\n",
       "14    -207.550629 -105.472603       5        1   8.377289\n",
       "5       52.817650   14.603765       6        1   7.916315\n",
       "1      168.010849    6.806950       7        1   7.694903\n",
       "2      -94.169601 -157.789551       8        1   6.392206\n",
       "3     -122.849174  -39.059418       9        1   4.928441\n",
       "0      -94.894264  195.408295      10        1   4.373819\n",
       "12      24.927401 -180.464874      11        1   4.276416\n",
       "10     -53.413467   26.832108      12        1   3.048783\n",
       "9      -12.255850  -72.093201      13        1   2.865475\n",
       "11    -224.217957   28.070581      14        1   2.592440\n",
       "13      36.154255  209.427582      15        1   2.318606, topic_info=        Term        Freq       Total Category  logprob  loglift\n",
       "15    debate  108.000000  108.000000  Default  30.0000  30.0000\n",
       "16  election  359.000000  359.000000  Default  29.0000  29.0000\n",
       "77     trump   75.000000   75.000000  Default  28.0000  28.0000\n",
       "2        amp   43.000000   43.000000  Default  27.0000  27.0000\n",
       "25     https  106.000000  106.000000  Default  26.0000  26.0000\n",
       "..       ...         ...         ...      ...      ...      ...\n",
       "30      know    0.086239   23.794367  Topic15  -6.5141  -1.8559\n",
       "47     point    0.086234   18.276805  Topic15  -6.5141  -1.5921\n",
       "23      help    0.086231   17.363382  Topic15  -6.5142  -1.5409\n",
       "89       way    0.086231   16.286004  Topic15  -6.5142  -1.4768\n",
       "48    policy    0.086230   20.259626  Topic15  -6.5142  -1.6952\n",
       "\n",
       "[701 rows x 6 columns], token_table=      Topic      Freq            Term\n",
       "term                                 \n",
       "0         1  0.887647  administration\n",
       "1         1  0.380366        american\n",
       "1         3  0.489043        american\n",
       "1         5  0.108676        american\n",
       "2         9  0.978486             amp\n",
       "...     ...       ...             ...\n",
       "90        3  0.714946             win\n",
       "90        4  0.259980             win\n",
       "91        9  0.942234            work\n",
       "92        2  0.921893           world\n",
       "93        6  0.958273            year\n",
       "\n",
       "[162 rows x 3 columns], R=30, lambda_step=0.01, plot_opts={'xlab': 'PC1', 'ylab': 'PC2'}, topic_order=[9, 8, 5, 7, 15, 6, 2, 3, 4, 1, 13, 11, 10, 12, 14])"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pyLDAvis\n",
    "import pyLDAvis.sklearn\n",
    "\n",
    "#Visualize with pyLDAvis\n",
    "\n",
    "pyLDAvis.enable_notebook()\n",
    "panel = pyLDAvis.sklearn.prepare(lda_model,data_vectorized,vectorizer,mds='tsne')\n",
    "panel"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Grid Search for Model Optimization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/vasusingh/opt/anaconda3/envs/twitter-analytics/lib/python3.8/site-packages/ipykernel/ipkernel.py:287: DeprecationWarning: `should_run_async` will not call `transform_cell` automatically in the future. Please pass the result to `transformed_cell` argument and any exception that happen during thetransform in `preprocessing_exc_tuple` in IPython 7.17 and above.\n",
      "  and should_run_async(code)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "GridSearchCV(estimator=LatentDirichletAllocation(),\n",
       "             param_grid={'learning_decay': [0.5, 0.7, 0.9],\n",
       "                         'n_components': [10, 15, 20, 25, 30]})"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Grid Search for Best LDA Model\n",
    "\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "\n",
    "# Define Search Param\n",
    "params = {'n_components' : [10,15,20,25,30], 'learning_decay' : [0.5,0.7,0.9]}\n",
    "\n",
    "# Model\n",
    "lda = LDA()\n",
    "\n",
    "# Grid Search\n",
    "model = GridSearchCV(lda,param_grid=params)\n",
    "\n",
    "# Perform Grid Search\n",
    "model.fit(data_vectorized)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best Model's Params:  {'learning_decay': 0.9, 'n_components': 10}\n",
      "Best Log Likelihood Score:  -2934.108929574632\n",
      "Model Perplexity:  82.09317100410958\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/vasusingh/opt/anaconda3/envs/twitter-analytics/lib/python3.8/site-packages/ipykernel/ipkernel.py:287: DeprecationWarning: `should_run_async` will not call `transform_cell` automatically in the future. Please pass the result to `transformed_cell` argument and any exception that happen during thetransform in `preprocessing_exc_tuple` in IPython 7.17 and above.\n",
      "  and should_run_async(code)\n"
     ]
    }
   ],
   "source": [
    "# Best Model\n",
    "best_lda_model = model.best_estimator_\n",
    "\n",
    "# Model Parameters\n",
    "print(\"Best Model's Params: \", model.best_params_)\n",
    "\n",
    "# Log Likelihood Score\n",
    "print(\"Best Log Likelihood Score: \", model.best_score_)\n",
    "\n",
    "# Perplexity\n",
    "print(\"Model Perplexity: \", best_lda_model.perplexity(data_vectorized))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Visualizing Top N Best Words"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/vasusingh/opt/anaconda3/envs/twitter-analytics/lib/python3.8/site-packages/ipykernel/ipkernel.py:287: DeprecationWarning: `should_run_async` will not call `transform_cell` automatically in the future. Please pass the result to `transformed_cell` argument and any exception that happen during thetransform in `preprocessing_exc_tuple` in IPython 7.17 and above.\n",
      "  and should_run_async(code)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "\n",
       "<link rel=\"stylesheet\" type=\"text/css\" href=\"https://cdn.rawgit.com/bmabey/pyLDAvis/files/ldavis.v1.0.0.css\">\n",
       "\n",
       "\n",
       "<div id=\"ldavis_el16161404848628756803121359262\"></div>\n",
       "<script type=\"text/javascript\">\n",
       "\n",
       "var ldavis_el16161404848628756803121359262_data = {\"mdsDat\": {\"x\": [32.42095184326172, 0.648955225944519, 71.01968383789062, 3.7859814167022705, -42.666500091552734, -3.6216893196105957, -3.632401704788208, -36.58440017700195, 44.391380310058594, 40.235809326171875], \"y\": [8.115229606628418, 71.70709991455078, 9.971006393432617, 33.18513488769531, -9.22206974029541, -43.52651596069336, -4.171707630157471, 35.804412841796875, 50.61538314819336, -31.591665267944336], \"topics\": [1, 2, 3, 4, 5, 6, 7, 8, 9, 10], \"cluster\": [1, 1, 1, 1, 1, 1, 1, 1, 1, 1], \"Freq\": [11.79035764248871, 11.655146505229792, 11.614584638546113, 11.521207572396193, 10.367522767774819, 9.706929788104414, 9.349462423022752, 9.224193155204992, 8.063101487093467, 6.707494020138732]}, \"tinfo\": {\"Term\": [\"debate\", \"presidential\", \"trump\", \"https\", \"win\", \"live\", \"election\", \"vote\", \"vice\", \"fly\", \"think\", \"want\", \"update\", \"people\", \"country\", \"voter\", 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\"amp\", \"amp\", \"amp\", \"ask\", \"ask\", \"ask\", \"ballot\", \"biden\", \"biden\", \"biden\", \"candidate\", \"candidate\", \"candidate\", \"candidate\", \"care\", \"care\", \"care\", \"change\", \"change\", \"change\", \"change\", \"clash\", \"clash\", \"close\", \"close\", \"close\", \"come\", \"come\", \"come\", \"country\", \"covid\", \"covid\", \"covid\", \"day\", \"day\", \"day\", \"debate\", \"debate\", \"debate\", \"election\", \"election\", \"election\", \"election\", \"election\", \"election\", \"election\", \"election\", \"election\", \"end\", \"end\", \"fact\", \"fact\", \"fact\", \"fly\", \"free\", \"free\", \"free\", \"good\", \"good\", \"good\", \"great\", \"great\", \"great\", \"help\", \"help\", \"help\", \"help\", \"history\", \"history\", \"history\", \"history\", \"https\", \"https\", \"https\", \"https\", \"https\", \"https\", \"important\", \"important\", \"important\", \"important\", \"important\", \"interfere\", \"just\", \"just\", \"just\", \"just\", \"just\", \"justice\", \"justice\", \"know\", \"know\", \"know\", \"leader\", \"leader\", \"leader\", \"let\", \"let\", \"let\", \"let\", \"live\", \"long\", \"long\", \"long\", \"lose\", \"lose\", \"mail\", \"make\", \"make\", \"make\", \"make\", \"make\", \"man\", \"man\", \"man\", \"man\", \"need\", \"need\", \"new\", \"new\", \"new\", \"new\", \"pandemic\", \"pandemic\", \"pandemic\", \"party\", \"party\", \"pence\", \"pence\", \"pence\", \"pence\", \"penny\", \"penny\", \"penny\", \"people\", \"people\", \"people\", \"people\", \"people\", \"people\", \"plan\", \"plan\", \"point\", \"point\", \"point\", \"policy\", \"policy\", \"politic\", \"politic\", \"politic\", \"politic\", \"political\", \"poll\", \"poll\", \"poll\", \"poll\", \"president\", \"presidential\", \"presidential\", \"public\", \"public\", \"question\", \"question\", \"ready\", \"ready\", \"realdonaldtrump\", \"realdonaldtrump\", \"realdonaldtrump\", \"really\", \"really\", \"response\", \"response\", \"result\", \"result\", \"result\", \"right\", \"right\", \"right\", \"rule\", \"rule\", \"rule\", \"rule\", \"russian\", \"russian\", \"say\", \"say\", \"say\", \"say\", \"say\", \"say\", \"send\", \"speak\", \"speak\", \"speak\", \"speak\", \"state\", \"state\", \"state\", \"stop\", \"stop\", \"support\", \"support\", \"talk\", \"talk\", \"talk\", \"tell\", \"tell\", \"thing\", \"thing\", \"thing\", \"thing\", \"thing\", \"thing\", \"think\", \"time\", \"time\", \"time\", \"time\", \"today\", \"today\", \"track\", \"trump\", \"trump\", \"trump\", \"trump\", \"trump\", \"try\", \"try\", \"upcoming\", \"upcoming\", \"update\", \"use\", \"use\", \"use\", \"vice\", \"vice\", \"vote\", \"vote\", \"vote\", \"vote\", \"voter\", \"voter\", \"voting\", \"vpdebate\", \"vpdebate\", \"vpdebate\", \"want\", \"want\", \"watch\", \"way\", \"way\", \"way\", \"way\", \"win\", \"work\", \"work\", \"work\", \"world\", \"world\", \"world\", \"year\"]}, \"R\": 30, \"lambda.step\": 0.01, \"plot.opts\": {\"xlab\": \"PC1\", \"ylab\": \"PC2\"}, \"topic.order\": [4, 2, 8, 9, 10, 1, 6, 5, 3, 7]};\n",
       "\n",
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       "   require([\"d3\"], function(d3){\n",
       "      window.d3 = d3;\n",
       "      LDAvis_load_lib(\"https://cdn.rawgit.com/bmabey/pyLDAvis/files/ldavis.v1.0.0.js\", function(){\n",
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       "    LDAvis_load_lib(\"https://cdnjs.cloudflare.com/ajax/libs/d3/3.5.5/d3.min.js\", function(){\n",
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      "text/plain": [
       "PreparedData(topic_coordinates=               x          y  topics  cluster       Freq\n",
       "topic                                                  \n",
       "3      32.420952   8.115230       1        1  11.790358\n",
       "1       0.648955  71.707100       2        1  11.655147\n",
       "7      71.019684   9.971006       3        1  11.614585\n",
       "8       3.785981  33.185135       4        1  11.521208\n",
       "9     -42.666500  -9.222070       5        1  10.367523\n",
       "0      -3.621689 -43.526516       6        1   9.706930\n",
       "5      -3.632402  -4.171708       7        1   9.349462\n",
       "4     -36.584400  35.804413       8        1   9.224193\n",
       "2      44.391380  50.615383       9        1   8.063101\n",
       "6      40.235809 -31.591665      10        1   6.707494, topic_info=            Term        Freq       Total Category  logprob  loglift\n",
       "15        debate  111.000000  111.000000  Default  30.0000  30.0000\n",
       "53  presidential   55.000000   55.000000  Default  29.0000  29.0000\n",
       "77         trump   80.000000   80.000000  Default  28.0000  28.0000\n",
       "25         https  110.000000  110.000000  Default  27.0000  27.0000\n",
       "90           win   49.000000   49.000000  Default  26.0000  26.0000\n",
       "..           ...         ...         ...      ...      ...      ...\n",
       "66         speak    0.103932   13.692565  Topic10  -7.3897  -2.1789\n",
       "38           man    0.103930   12.645850  Topic10  -7.3897  -2.0994\n",
       "8         change    0.103930   18.491876  Topic10  -7.3897  -2.4794\n",
       "6      candidate    0.103926   23.032306  Topic10  -7.3898  -2.6990\n",
       "41      pandemic    0.103921   14.049281  Topic10  -7.3898  -2.2048\n",
       "\n",
       "[367 rows x 6 columns], token_table=      Topic      Freq            Term\n",
       "term                                 \n",
       "0         1  0.442661  administration\n",
       "0         5  0.354129  administration\n",
       "0         7  0.177065  administration\n",
       "1         1  0.312697        american\n",
       "1         2  0.104232        american\n",
       "...     ...       ...             ...\n",
       "91       10  0.078670            work\n",
       "92        3  0.691188           world\n",
       "92        4  0.069119           world\n",
       "92        8  0.138238           world\n",
       "93        5  0.943302            year\n",
       "\n",
       "[269 rows x 3 columns], R=30, lambda_step=0.01, plot_opts={'xlab': 'PC1', 'ylab': 'PC2'}, topic_order=[4, 2, 8, 9, 10, 1, 6, 5, 3, 7])"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Visualize with pyLDAvis\n",
    "\n",
    "pyLDAvis.enable_notebook()\n",
    "panel = pyLDAvis.sklearn.prepare(best_lda_model,data_vectorized,vectorizer,mds='tsne')\n",
    "panel"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Show top n keywords for each topic\n",
    "\n",
    "\n",
    "\n",
    "topic_keywords = show_topics()        \n",
    "\n",
    "# Topic - Keywords Dataframe\n",
    "df_topic_keywords = \n",
    "df_topic_keywords.columns = \n",
    "df_topic_keywords.index = \n",
    "df_topic_keywords"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 3.Topic Modelling"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Automatically extract what topics people are discussing from large volumes of text"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Topics found via LDA:\n",
      "\n",
      "Topic #0:\n",
      "elections trump vote election pence\n",
      "\n",
      "Topic #1:\n",
      "elections win debate presidential vote\n",
      "\n",
      "Topic #2:\n",
      "harris pence elections debate trump\n",
      "\n",
      "Topic #3:\n",
      "elections trump america vote entitled\n",
      "\n",
      "Topic #4:\n",
      "debate pence harris elections trump\n",
      "\n",
      "Topic #5:\n",
      "elections people trump want says\n",
      "\n",
      "Topic #6:\n",
      "elections trump know let think\n"
     ]
    }
   ],
   "source": [
    "# Initialise the count vectorizer with the English stop words\n",
    "count_vectorizer = CountVectorizer(stop_words='english')\n",
    "# Fit and transform the processed titles to give a DTM\n",
    "count_data = count_vectorizer.fit_transform(cleaned_tweets['ProcessedTweets'])\n",
    "\n",
    "import warnings\n",
    "warnings.simplefilter('ignore',DeprecationWarning)\n",
    "\n",
    "def print_topics(model,count_vectorizer,n_top_words):\n",
    "    words = count_vectorizer.get_feature_names()\n",
    "    for topic_idx,topic in enumerate(model.components_):\n",
    "        print(\"\\nTopic #%d:\" % topic_idx)\n",
    "        print(\" \".join([words[i] for i in topic.argsort()[:-n_top_words - 1:-1]]))\n",
    "\n",
    "# Tweak the two parameters below\n",
    "\n",
    "number_topics = 15\n",
    "number_words = 5\n",
    "\n",
    "# Create and fit the LDA model\n",
    "\n",
    "lda = LDA(n_components=number_topics, n_jobs=-1)\n",
    "lda.fit(count_data)\n",
    "# Print the topics found by the LDA model\n",
    "\n",
    "print(\"Topics found via LDA:\")\n",
    "print_topics(lda, count_vectorizer, number_words)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###### Note : The above results can be improved by using n-grams"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 4. Sentiment Score"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Sentiment analysis helps us identify emotions through social media texts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Average Polarity Score : 0.046\n"
     ]
    }
   ],
   "source": [
    "def senti(x):\n",
    "    return textblob.TextBlob(x).sentiment.polarity\n",
    "\n",
    "d1 = pd.DataFrame()\n",
    "d1['Tweet'] = final_df['Tweet']\n",
    "d1['Sentiment-Polarity'] = final_df['Tweet'].apply(senti)\n",
    "\n",
    "avg_score = np.average(d1['Sentiment-Polarity'])\n",
    "print('Average Polarity Score : ' +str(round(avg_score,3)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Average vader Score : -0.79\n"
     ]
    }
   ],
   "source": [
    "from vaderSentiment.vaderSentiment import SentimentIntensityAnalyzer\n",
    "analyser = SentimentIntensityAnalyzer()\n",
    "\n",
    "def sentiment_analyzer_scores(sentence):\n",
    "    l = []\n",
    "    score = analyser.polarity_scores(sentence)\n",
    "    l.append(score)\n",
    "    return l\n",
    "    \n",
    "d1['vaderScore'] = final_df['Tweet'].apply(lambda x: sentiment_analyzer_scores(x))\n",
    "\n",
    "d1['vaderScore'] = d1['vaderScore'][0][0]['compound']\n",
    "\n",
    "avg_score = np.average(d1['vaderScore'])\n",
    "print('Average vader Score : ' +str(round(avg_score,3)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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