Election_Analysis_TrumpBiden.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "ename": "ModuleNotFoundError",
     "evalue": "No module named 'textblob'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mModuleNotFoundError\u001b[0m                       Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-3-e98630e1551b>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mos\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mtweepy\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mtw\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 6\u001b[0;31m \u001b[0;32mfrom\u001b[0m \u001b[0mtextblob\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mTextBlob\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      7\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      8\u001b[0m \u001b[0msleep_on_rate_limit\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mModuleNotFoundError\u001b[0m: No module named 'textblob'"
     ]
    }
   ],
   "source": [
    "import tweepy\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import os\n",
    "import tweepy as tw\n",
    "from textblob import TextBlob\n",
    "\n",
    "sleep_on_rate_limit=False"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>geo</th>\n",
       "      <th>text</th>\n",
       "      <th>user</th>\n",
       "      <th>location</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>None</td>\n",
       "      <td>RT @TeaPainUSA: The hit dogs are hollerin’. ht...</td>\n",
       "      <td>RealDonCheeto</td>\n",
       "      <td>Yes I Voted</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>None</td>\n",
       "      <td>RT @WayneDupreeShow: \"At this point - Biden pr...</td>\n",
       "      <td>WhipperSnacker</td>\n",
       "      <td>South Carolina</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>None</td>\n",
       "      <td>RT @JJDJ1187: Joe Biden gets into 'trouble,' s...</td>\n",
       "      <td>SAndricsak</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>None</td>\n",
       "      <td>RT @ACTBrigitte: Is Joe Biden the worst presid...</td>\n",
       "      <td>McRN101</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>None</td>\n",
       "      <td>RT @KimDotcom: “The future belongs to America....</td>\n",
       "      <td>CJDanielsFarms</td>\n",
       "      <td>NW Minnesota Farmer 🚜</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    geo                                               text            user  \\\n",
       "0  None  RT @TeaPainUSA: The hit dogs are hollerin’. ht...   RealDonCheeto   \n",
       "1  None  RT @WayneDupreeShow: \"At this point - Biden pr...  WhipperSnacker   \n",
       "2  None  RT @JJDJ1187: Joe Biden gets into 'trouble,' s...      SAndricsak   \n",
       "3  None  RT @ACTBrigitte: Is Joe Biden the worst presid...         McRN101   \n",
       "4  None  RT @KimDotcom: “The future belongs to America....  CJDanielsFarms   \n",
       "\n",
       "                location  \n",
       "0            Yes I Voted  \n",
       "1         South Carolina  \n",
       "2                         \n",
       "3                         \n",
       "4  NW Minnesota Farmer 🚜  "
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "consumer_key =\"t37dWre7nh5Zl68H0Mhnd5Ptt\"\n",
    "consumer_key_secret=\"rBlGiKaN9RmsY7b2nq0c9wUUPfrSn4uKC8egA5DS0HKD3c6RiH\"\n",
    "access_token=\"1369718031769759746-a8vE51K7o8oLf6fD1XNKOnDwz4Gjaz\"\n",
    "access_token_secret= \"FR2l4XIdk26k8DEaqqsBVkAXpD9RqAoehRtOvO6eJVRpG\"\n",
    "# Setup tweepy with Twitter credentials\n",
    "auth = tweepy.OAuthHandler(consumer_key, consumer_key_secret)\n",
    "auth.set_access_token(access_token, access_token_secret)\n",
    "api = tweepy.API(auth, wait_on_rate_limit=True, wait_on_rate_limit_notify=True)\n",
    "\n",
    "search_words = \"Joe Biden\"\n",
    "date_since = \"2020-10-27\"\n",
    "tweets = tw.Cursor(api.search, q=search_words, lang=\"en\", since=date_since).items(500)\n",
    "tweets\n",
    "tweet_details = [[tweet.geo, tweet.text, tweet.user.screen_name, tweet.user.location] for tweet in tweets]\n",
    "tweet_df_biden = pd.DataFrame(data=tweet_details, columns=[\"geo\",\"text\",\"user\",\"location\"])\n",
    "tweet_df_biden.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
     "ename": "TypeError",
     "evalue": "'numpy.int64' object is not callable",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-91-c541a8915c92>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mtweet_df_biden\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msize\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;31mTypeError\u001b[0m: 'numpy.int64' object is not callable"
     ]
    }
   ],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>geo</th>\n",
       "      <th>text</th>\n",
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       "      <th>Subject</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>None</td>\n",
       "      <td>The hit dogs are hollerin’.</td>\n",
       "      <td>RealDonCheeto</td>\n",
       "      <td>Yes I Voted</td>\n",
       "      <td>Joe Biden</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>None</td>\n",
       "      <td>\"At this point - Biden probably got 8.2 milli...</td>\n",
       "      <td>WhipperSnacker</td>\n",
       "      <td>South Carolina</td>\n",
       "      <td>Joe Biden</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>None</td>\n",
       "      <td>Joe Biden gets into 'trouble,' spends 30 seco...</td>\n",
       "      <td>SAndricsak</td>\n",
       "      <td></td>\n",
       "      <td>Joe Biden</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>None</td>\n",
       "      <td>Is Joe Biden the worst president in US histor...</td>\n",
       "      <td>McRN101</td>\n",
       "      <td></td>\n",
       "      <td>Joe Biden</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>None</td>\n",
       "      <td>“The future belongs to America.”— President J...</td>\n",
       "      <td>CJDanielsFarms</td>\n",
       "      <td>NW Minnesota Farmer 🚜</td>\n",
       "      <td>Joe Biden</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>None</td>\n",
       "      <td>Nicole Kidman portraying Lucille Ball...........</td>\n",
       "      <td>poppytim24</td>\n",
       "      <td>Missouri, USA</td>\n",
       "      <td>Joe Biden</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6</td>\n",
       "      <td>None</td>\n",
       "      <td>Keystone XL is nicknamed the ‘Zombie Pipeline...</td>\n",
       "      <td>ActivistHat_</td>\n",
       "      <td></td>\n",
       "      <td>Joe Biden</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7</td>\n",
       "      <td>None</td>\n",
       "      <td>Joe Biden and Jimmy Carter, 1979: 📷: Biden's ...</td>\n",
       "      <td>wdrohrer</td>\n",
       "      <td>Vancouver, British Columbia</td>\n",
       "      <td>Joe Biden</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>8</td>\n",
       "      <td>None</td>\n",
       "      <td>Joe Biden said \" White Supremacy is Terrorism...</td>\n",
       "      <td>LandmarkCarol</td>\n",
       "      <td></td>\n",
       "      <td>Joe Biden</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>9</td>\n",
       "      <td>None</td>\n",
       "      <td>Everything Joe Biden says about guns is idiotic.</td>\n",
       "      <td>kinseiji8</td>\n",
       "      <td></td>\n",
       "      <td>Joe Biden</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>10</td>\n",
       "      <td>None</td>\n",
       "      <td>In breaking NFL Draft News, Joe Biden has chos...</td>\n",
       "      <td>stranahan</td>\n",
       "      <td>Sioux Falls, SD</td>\n",
       "      <td>Joe Biden</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>11</td>\n",
       "      <td>None</td>\n",
       "      <td>Still waiting for doses to be available for m...</td>\n",
       "      <td>vpavarala</td>\n",
       "      <td>Hyderabad, India</td>\n",
       "      <td>Joe Biden</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>12</td>\n",
       "      <td>None</td>\n",
       "      <td>\"A very catholic president...\"As a catholic I...</td>\n",
       "      <td>LobeznoMestizo</td>\n",
       "      <td>Cuernavaca, Morelos 🇲🇽</td>\n",
       "      <td>Joe Biden</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>13</td>\n",
       "      <td>None</td>\n",
       "      <td>This writer for the Financial Times stating c...</td>\n",
       "      <td>sci_n_politics</td>\n",
       "      <td></td>\n",
       "      <td>Joe Biden</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>14</td>\n",
       "      <td>None</td>\n",
       "      <td>Everything Joe Biden says about guns is idiotic.</td>\n",
       "      <td>45acpjoe</td>\n",
       "      <td>Skull Island</td>\n",
       "      <td>Joe Biden</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>15</td>\n",
       "      <td>None</td>\n",
       "      <td>Joe Biden gets into 'trouble,' spends 30 secon...</td>\n",
       "      <td>duanepoole</td>\n",
       "      <td></td>\n",
       "      <td>Joe Biden</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>16</td>\n",
       "      <td>None</td>\n",
       "      <td>Joe Biden's ratings are quite bad. The proble...</td>\n",
       "      <td>MelanyL08249168</td>\n",
       "      <td></td>\n",
       "      <td>Joe Biden</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>17</td>\n",
       "      <td>None</td>\n",
       "      <td>The Joe Biden administration announced that t...</td>\n",
       "      <td>tsukasakotobuki</td>\n",
       "      <td>Ishikawa, Japan</td>\n",
       "      <td>Joe Biden</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>18</td>\n",
       "      <td>None</td>\n",
       "      <td>What makes Joe Biden such a good President is...</td>\n",
       "      <td>POTUS45stinks</td>\n",
       "      <td>Oregon, USA</td>\n",
       "      <td>Joe Biden</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>19</td>\n",
       "      <td>None</td>\n",
       "      <td>Everything Joe Biden says about guns is idiotic.</td>\n",
       "      <td>VACountryBoy94</td>\n",
       "      <td>Virginia, USA</td>\n",
       "      <td>Joe Biden</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     geo                                               text             user  \\\n",
       "0   None                       The hit dogs are hollerin’.     RealDonCheeto   \n",
       "1   None   \"At this point - Biden probably got 8.2 milli...   WhipperSnacker   \n",
       "2   None   Joe Biden gets into 'trouble,' spends 30 seco...       SAndricsak   \n",
       "3   None   Is Joe Biden the worst president in US histor...          McRN101   \n",
       "4   None   “The future belongs to America.”— President J...   CJDanielsFarms   \n",
       "5   None   Nicole Kidman portraying Lucille Ball...........       poppytim24   \n",
       "6   None   Keystone XL is nicknamed the ‘Zombie Pipeline...     ActivistHat_   \n",
       "7   None   Joe Biden and Jimmy Carter, 1979: 📷: Biden's ...         wdrohrer   \n",
       "8   None   Joe Biden said \" White Supremacy is Terrorism...    LandmarkCarol   \n",
       "9   None   Everything Joe Biden says about guns is idiotic.        kinseiji8   \n",
       "10  None  In breaking NFL Draft News, Joe Biden has chos...        stranahan   \n",
       "11  None   Still waiting for doses to be available for m...        vpavarala   \n",
       "12  None   \"A very catholic president...\"As a catholic I...   LobeznoMestizo   \n",
       "13  None   This writer for the Financial Times stating c...   sci_n_politics   \n",
       "14  None   Everything Joe Biden says about guns is idiotic.         45acpjoe   \n",
       "15  None  Joe Biden gets into 'trouble,' spends 30 secon...       duanepoole   \n",
       "16  None   Joe Biden's ratings are quite bad. The proble...  MelanyL08249168   \n",
       "17  None   The Joe Biden administration announced that t...  tsukasakotobuki   \n",
       "18  None   What makes Joe Biden such a good President is...    POTUS45stinks   \n",
       "19  None   Everything Joe Biden says about guns is idiotic.   VACountryBoy94   \n",
       "\n",
       "                       location    Subject  \n",
       "0                   Yes I Voted  Joe Biden  \n",
       "1                South Carolina  Joe Biden  \n",
       "2                                Joe Biden  \n",
       "3                                Joe Biden  \n",
       "4         NW Minnesota Farmer 🚜  Joe Biden  \n",
       "5                 Missouri, USA  Joe Biden  \n",
       "6                                Joe Biden  \n",
       "7   Vancouver, British Columbia  Joe Biden  \n",
       "8                                Joe Biden  \n",
       "9                                Joe Biden  \n",
       "10              Sioux Falls, SD  Joe Biden  \n",
       "11             Hyderabad, India  Joe Biden  \n",
       "12       Cuernavaca, Morelos 🇲🇽  Joe Biden  \n",
       "13                               Joe Biden  \n",
       "14                 Skull Island  Joe Biden  \n",
       "15                               Joe Biden  \n",
       "16                               Joe Biden  \n",
       "17              Ishikawa, Japan  Joe Biden  \n",
       "18                  Oregon, USA  Joe Biden  \n",
       "19                Virginia, USA  Joe Biden  "
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import re\n",
    "def clean_tweets(text):\n",
    "  text = re.sub(\"RT @[\\w]*:\",\"\",text)\n",
    "  text = re.sub(\"@[\\w]*\",\"\",text)\n",
    "  text = re.sub(\"https?://[A-Za-z0-9./]*\",\"\",text)\n",
    "  text = re.sub(\"\\n\",\"\",text)\n",
    "  return text\n",
    "tweet_df_biden['text'] = tweet_df_biden['text'].apply(lambda x: clean_tweets(x))\n",
    "tweet_df_biden['Subject'] = search_words\n",
    "tweet_df_biden.head(20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Rate limit reached. Sleeping for: 35\n"
     ]
    },
    {
     "data": {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>geo</th>\n",
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       "      <th>user</th>\n",
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       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>None</td>\n",
       "      <td>Latest from Donald Trump: 2024 and Running Mat...</td>\n",
       "      <td>janel_savage</td>\n",
       "      <td>, USA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>None</td>\n",
       "      <td>RT @WIONews: Former US president and reality s...</td>\n",
       "      <td>munishkagg</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>None</td>\n",
       "      <td>@travisakers @thedailybeast So Greenberg offer...</td>\n",
       "      <td>bchadhill</td>\n",
       "      <td>New Orleans, LA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>None</td>\n",
       "      <td>RT @SpiroAgnewGhost: Watching the recent news ...</td>\n",
       "      <td>dhighl5184</td>\n",
       "      <td>Black Hawk</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>None</td>\n",
       "      <td>RT @ProjectLincoln: .@GOPLeader Kevin McCarthy...</td>\n",
       "      <td>mamahasaheadac1</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    geo                                               text             user  \\\n",
       "0  None  Latest from Donald Trump: 2024 and Running Mat...     janel_savage   \n",
       "1  None  RT @WIONews: Former US president and reality s...       munishkagg   \n",
       "2  None  @travisakers @thedailybeast So Greenberg offer...        bchadhill   \n",
       "3  None  RT @SpiroAgnewGhost: Watching the recent news ...       dhighl5184   \n",
       "4  None  RT @ProjectLincoln: .@GOPLeader Kevin McCarthy...  mamahasaheadac1   \n",
       "\n",
       "          location  \n",
       "0            , USA  \n",
       "1                   \n",
       "2  New Orleans, LA  \n",
       "3       Black Hawk  \n",
       "4                   "
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "search_words = \"Donald Trump\"\n",
    "date_since = \"2020-10-27\"\n",
    "tweets = tw.Cursor(api.search, q=search_words, lang=\"en\", since=date_since).items(500)\n",
    "tweets\n",
    "tweet_details = [[tweet.geo, tweet.text, tweet.user.screen_name, tweet.user.location] for tweet in tweets]\n",
    "tweet_df_trump = pd.DataFrame(data=tweet_details, columns=[\"geo\",\"text\",\"user\",\"location\"])\n",
    "tweet_df_trump.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <td>janel_savage</td>\n",
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       "      <td>Donald Trump</td>\n",
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       "      <td>Former US president and reality show star, Do...</td>\n",
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       "      <td></td>\n",
       "      <td>Donald Trump</td>\n",
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       "      <td>So Greenberg offered Stone $250k in essentia...</td>\n",
       "      <td>bchadhill</td>\n",
       "      <td>New Orleans, LA</td>\n",
       "      <td>Donald Trump</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>None</td>\n",
       "      <td>Watching the recent news lately &amp;amp; connect...</td>\n",
       "      <td>dhighl5184</td>\n",
       "      <td>Black Hawk</td>\n",
       "      <td>Donald Trump</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>None</td>\n",
       "      <td>. Kevin McCarthy wants us to forget what we a...</td>\n",
       "      <td>mamahasaheadac1</td>\n",
       "      <td></td>\n",
       "      <td>Donald Trump</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>None</td>\n",
       "      <td>Kevin McCarthy wants us to forget what we all...</td>\n",
       "      <td>leelee24305982</td>\n",
       "      <td></td>\n",
       "      <td>Donald Trump</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6</td>\n",
       "      <td>None</td>\n",
       "      <td>Search Warrants Move Feds Closer to Charging ...</td>\n",
       "      <td>MadVet2000</td>\n",
       "      <td>Melbourne, Victoria</td>\n",
       "      <td>Donald Trump</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7</td>\n",
       "      <td>None</td>\n",
       "      <td>Search Warrants Move Feds Closer to Charging ...</td>\n",
       "      <td>Diana_Chumley</td>\n",
       "      <td>Tennessee, USA</td>\n",
       "      <td>Donald Trump</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>8</td>\n",
       "      <td>None</td>\n",
       "      <td>It is not that Steve Bannon gleefully cherishe...</td>\n",
       "      <td>badpersonbot</td>\n",
       "      <td></td>\n",
       "      <td>Donald Trump</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>9</td>\n",
       "      <td>None</td>\n",
       "      <td>Here's with former President Donald Trump said...</td>\n",
       "      <td>gromerjeffers</td>\n",
       "      <td></td>\n",
       "      <td>Donald Trump</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>10</td>\n",
       "      <td>None</td>\n",
       "      <td>Search Warrants Move Feds Closer to Charging ...</td>\n",
       "      <td>juliejourneying</td>\n",
       "      <td>Sacramento</td>\n",
       "      <td>Donald Trump</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>11</td>\n",
       "      <td>None</td>\n",
       "      <td>Propaganda media behaved, relentlessly, as if...</td>\n",
       "      <td>Desertgramma1</td>\n",
       "      <td>Arizona, USA</td>\n",
       "      <td>Donald Trump</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>12</td>\n",
       "      <td>None</td>\n",
       "      <td>Most charges maybe for traspassing, but the...</td>\n",
       "      <td>BerthaAliciaO11</td>\n",
       "      <td></td>\n",
       "      <td>Donald Trump</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>13</td>\n",
       "      <td>None</td>\n",
       "      <td>This could be the lead-in every day:  Donald ...</td>\n",
       "      <td>martyq01</td>\n",
       "      <td>New Jersey</td>\n",
       "      <td>Donald Trump</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>14</td>\n",
       "      <td>None</td>\n",
       "      <td>No one in the GOP can hold a candle to Donald...</td>\n",
       "      <td>JackMul17128792</td>\n",
       "      <td></td>\n",
       "      <td>Donald Trump</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>15</td>\n",
       "      <td>None</td>\n",
       "      <td>it's a rough afternoon to be a QAnon believer...</td>\n",
       "      <td>Pisces_February</td>\n",
       "      <td>West Village, New York City</td>\n",
       "      <td>Donald Trump</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>16</td>\n",
       "      <td>None</td>\n",
       "      <td>Donald Trump, twice impeached, criminal activ...</td>\n",
       "      <td>rradgoski</td>\n",
       "      <td>Scranton, PA</td>\n",
       "      <td>Donald Trump</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>17</td>\n",
       "      <td>None</td>\n",
       "      <td>Search Warrants Move Feds Closer to Charging ...</td>\n",
       "      <td>angelbeech59</td>\n",
       "      <td></td>\n",
       "      <td>Donald Trump</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>18</td>\n",
       "      <td>None</td>\n",
       "      <td>Donald Trump and Ron DeSantis will not be on ...</td>\n",
       "      <td>loserandsucker2</td>\n",
       "      <td></td>\n",
       "      <td>Donald Trump</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>19</td>\n",
       "      <td>None</td>\n",
       "      <td>Donald Trump and Ron DeSantis will not be on ...</td>\n",
       "      <td>HoundsLou</td>\n",
       "      <td>Colorado, USA</td>\n",
       "      <td>Donald Trump</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     geo                                               text             user  \\\n",
       "0   None  Latest from Donald Trump: 2024 and Running Mat...     janel_savage   \n",
       "1   None   Former US president and reality show star, Do...       munishkagg   \n",
       "2   None    So Greenberg offered Stone $250k in essentia...        bchadhill   \n",
       "3   None   Watching the recent news lately &amp; connect...       dhighl5184   \n",
       "4   None   . Kevin McCarthy wants us to forget what we a...  mamahasaheadac1   \n",
       "5   None   Kevin McCarthy wants us to forget what we all...   leelee24305982   \n",
       "6   None   Search Warrants Move Feds Closer to Charging ...       MadVet2000   \n",
       "7   None   Search Warrants Move Feds Closer to Charging ...    Diana_Chumley   \n",
       "8   None  It is not that Steve Bannon gleefully cherishe...     badpersonbot   \n",
       "9   None  Here's with former President Donald Trump said...    gromerjeffers   \n",
       "10  None   Search Warrants Move Feds Closer to Charging ...  juliejourneying   \n",
       "11  None   Propaganda media behaved, relentlessly, as if...    Desertgramma1   \n",
       "12  None     Most charges maybe for traspassing, but the...  BerthaAliciaO11   \n",
       "13  None   This could be the lead-in every day:  Donald ...         martyq01   \n",
       "14  None   No one in the GOP can hold a candle to Donald...  JackMul17128792   \n",
       "15  None   it's a rough afternoon to be a QAnon believer...  Pisces_February   \n",
       "16  None   Donald Trump, twice impeached, criminal activ...        rradgoski   \n",
       "17  None   Search Warrants Move Feds Closer to Charging ...     angelbeech59   \n",
       "18  None   Donald Trump and Ron DeSantis will not be on ...  loserandsucker2   \n",
       "19  None   Donald Trump and Ron DeSantis will not be on ...        HoundsLou   \n",
       "\n",
       "                       location       Subject  \n",
       "0                         , USA  Donald Trump  \n",
       "1                                Donald Trump  \n",
       "2               New Orleans, LA  Donald Trump  \n",
       "3                    Black Hawk  Donald Trump  \n",
       "4                                Donald Trump  \n",
       "5                                Donald Trump  \n",
       "6           Melbourne, Victoria  Donald Trump  \n",
       "7                Tennessee, USA  Donald Trump  \n",
       "8                                Donald Trump  \n",
       "9                                Donald Trump  \n",
       "10                  Sacramento   Donald Trump  \n",
       "11                 Arizona, USA  Donald Trump  \n",
       "12                               Donald Trump  \n",
       "13                   New Jersey  Donald Trump  \n",
       "14                               Donald Trump  \n",
       "15  West Village, New York City  Donald Trump  \n",
       "16                 Scranton, PA  Donald Trump  \n",
       "17                               Donald Trump  \n",
       "18                               Donald Trump  \n",
       "19                Colorado, USA  Donald Trump  "
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import re\n",
    "def clean_tweets(text):\n",
    "  text = re.sub(\"RT @[\\w]*:\",\"\",text)\n",
    "  text = re.sub(\"@[\\w]*\",\"\",text)\n",
    "  text = re.sub(\"https?://[A-Za-z0-9./]*\",\"\",text)\n",
    "  text = re.sub(\"\\n\",\"\",text)\n",
    "  return text\n",
    "tweet_df_trump['text'] = tweet_df_trump['text'].apply(lambda x: clean_tweets(x))\n",
    "tweet_df_trump['Subject'] = search_words\n",
    "tweet_df_trump.head(20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [],
   "source": [
    "#tweet_df_biden.append(tweet_df_trump, ignore_index = True)\n",
    "tweet_combined_df = pd.concat([tweet_df_trump, tweet_df_biden], ignore_index=True, sort=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "    states = ['Alabama', 'Alaska', 'Arizona', 'Arkansas', 'California', 'Colorado', 'Connecticut', 'Delaware', 'Florida', 'Georgia', 'Hawaii', 'Idaho', 'Illinois', 'Indiana', 'Iowa', 'Kansas', 'Kentucky', 'Louisiana', 'Maine', 'Maryland', 'Massachusetts', 'Michigan', 'Minnesota', 'Mississippi', 'Missouri', 'Montana', 'Nebraska', 'Nevada', 'New Hampshire', 'New Jersey', 'New York', 'New Mexico', 'North Carolina', 'North Dakota', 'Ohio', 'Oklahoma', 'Oregon', 'Pennsylvania', 'Rhode Island', 'South Carolina', 'South Dakota', 'Tennessee', 'Texas', 'Utah', 'Vermont', 'Virginia', 'Washington', 'West Virginia', 'Wisconsin', 'Wyoming']\n",
    "    stateCodes = ['AL', 'AK', 'AZ', 'AR', 'CA', 'CO', 'CT', 'DE', 'FL', 'GA', 'HI', 'ID', 'IL', 'IN', 'IA', 'KS', 'KY', 'LA', 'ME', 'MD', 'MA', 'MI', 'MN', 'MS', 'MO', 'MT', 'NE', 'NV', 'NH', 'NJ', 'NM', 'NY', 'NC', 'ND', 'OH', 'OK', 'OR', 'PA', 'RI', 'SC', 'SD', 'TN', 'TX', 'UT', 'VT', 'VA', 'WA', 'WV', 'WI', 'WY']\n",
    "    stateMapping = {'AL': 'Alabama', 'AK': 'Alaska', 'AZ': 'Arizona', 'AR': 'Arkansas', 'CA': 'California', 'CO': 'Colorado', 'CT': 'Connecticut', 'DE': 'Delaware', 'FL': 'Florida', 'GA': 'Georgia', \n",
    "                      'HI': 'Hawaii', 'ID': 'Idaho', 'IL': 'Illinois', 'IN': 'Indiana', 'IA': 'Iowa', 'KS': 'Kansas', 'KY': 'Kentucky', 'LA': 'Louisiana', 'ME': 'Maine', 'MD': 'Maryland', 'MA': 'Massachusetts', 'MI': 'Michigan', 'MN': 'Minnesota', 'MS': 'Mississippi', 'MO': 'Missouri', 'MT': 'Montana', 'NE': 'Nebraska', 'NV': 'Nevada', 'NH': 'New Hampshire', 'NJ': 'New Jersey', 'NY': 'New York', 'NM': 'New Mexico', 'NC': 'North Carolina', 'ND': 'North Dakota', 'OH': 'Ohio', 'OK': 'Oklahoma', 'OR': 'Oregon', 'PA': 'Pennsylvania', 'RI': 'Rhode Island', 'SC': 'South Carolina', 'SD': 'South Dakota', 'TN': 'Tennessee', 'TX': 'Texas', 'UT':  'Utah', 'VT': 'Vermont', 'VA': 'Virginia', 'WA': 'Washington', 'WV':  'West Virginia', 'WI': 'Wisconsin', 'WY': 'Wyoming'}\n",
    "    tweet_copied_df = tweet_combined_df\n",
    "    for index, row in tweet_combined_df.iterrows():\n",
    "      flag = 0\n",
    "      if row.location:\n",
    "        locationSplit = row.location.split(',')\n",
    "        for word in locationSplit:\n",
    "          word_stripped = word.strip()\n",
    "          if word_stripped in states:\n",
    "            flag = 1\n",
    "            row['state'] = word_stripped\n",
    "          elif word_stripped in stateCodes:\n",
    "            flag = 1\n",
    "            row['state'] = stateMapping[word_stripped]\n",
    "      if flag == 0:\n",
    "        tweet_copied_df = tweet_copied_df.drop(index=index)\n",
    "      else:\n",
    "        tweet_copied_df.loc[index, 'state'] = row['state']\n",
    "        \n",
    "\n",
    "        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [],
   "source": [
    "tweet_copied_df.to_csv('tweets_election.csv',mode='a')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[nltk_data] Downloading package vader_lexicon to\n",
      "[nltk_data]     /Users/likhithadivyani/nltk_data...\n",
      "[nltk_data]   Package vader_lexicon is already up-to-date!\n"
     ]
    }
   ],
   "source": [
    "import nltk\n",
    "from nltk.sentiment.vader import SentimentIntensityAnalyzer\n",
    "import pandas as pd\n",
    "nltk.download('vader_lexicon')\n",
    "sid = SentimentIntensityAnalyzer()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/anaconda3/lib/python3.7/site-packages/pandas/core/frame.py:4102: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  errors=errors,\n"
     ]
    },
    {
     "data": {
      "text/html": [
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       "      <td>Donald Trump</td>\n",
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       "    <tr>\n",
       "      <td>1</td>\n",
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       "      <td>Search Warrants Move Feds Closer to Charging ...</td>\n",
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       "      <td>Tennessee, USA</td>\n",
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       "      <td>2</td>\n",
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       "      <td>Arizona, USA</td>\n",
       "      <td>Donald Trump</td>\n",
       "      <td>Arizona</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>This could be the lead-in every day:  Donald ...</td>\n",
       "      <td>martyq01</td>\n",
       "      <td>New Jersey</td>\n",
       "      <td>Donald Trump</td>\n",
       "      <td>New Jersey</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Donald Trump, twice impeached, criminal activ...</td>\n",
       "      <td>rradgoski</td>\n",
       "      <td>Scranton, PA</td>\n",
       "      <td>Donald Trump</td>\n",
       "      <td>Pennsylvania</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   geo                                               text           user  \\\n",
       "0  NaN    So Greenberg offered Stone $250k in essentia...      bchadhill   \n",
       "1  NaN   Search Warrants Move Feds Closer to Charging ...  Diana_Chumley   \n",
       "2  NaN   Propaganda media behaved, relentlessly, as if...  Desertgramma1   \n",
       "3  NaN   This could be the lead-in every day:  Donald ...       martyq01   \n",
       "4  NaN   Donald Trump, twice impeached, criminal activ...      rradgoski   \n",
       "\n",
       "          location       Subject         state  \n",
       "0  New Orleans, LA  Donald Trump     Louisiana  \n",
       "1   Tennessee, USA  Donald Trump     Tennessee  \n",
       "2     Arizona, USA  Donald Trump       Arizona  \n",
       "3       New Jersey  Donald Trump    New Jersey  \n",
       "4     Scranton, PA  Donald Trump  Pennsylvania  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tweets_election_df = pd.read_csv('tweets_election.csv')\n",
    "tweets_trump = tweets_election_df[tweets_election_df.Subject == 'Donald Trump']\n",
    "tweets_trump.drop(tweets_trump.columns[0], axis=1, inplace = True)\n",
    "tweets_trump.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <tbody>\n",
       "    <tr>\n",
       "      <td>143</td>\n",
       "      <td>NaN</td>\n",
       "      <td>\"At this point - Biden probably got 8.2 milli...</td>\n",
       "      <td>WhipperSnacker</td>\n",
       "      <td>South Carolina</td>\n",
       "      <td>Joe Biden</td>\n",
       "      <td>South Carolina</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>144</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Nicole Kidman portraying Lucille Ball...........</td>\n",
       "      <td>poppytim24</td>\n",
       "      <td>Missouri, USA</td>\n",
       "      <td>Joe Biden</td>\n",
       "      <td>Missouri</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>145</td>\n",
       "      <td>NaN</td>\n",
       "      <td>In breaking NFL Draft News, Joe Biden has chos...</td>\n",
       "      <td>stranahan</td>\n",
       "      <td>Sioux Falls, SD</td>\n",
       "      <td>Joe Biden</td>\n",
       "      <td>South Dakota</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>146</td>\n",
       "      <td>NaN</td>\n",
       "      <td>What makes Joe Biden such a good President is...</td>\n",
       "      <td>POTUS45stinks</td>\n",
       "      <td>Oregon, USA</td>\n",
       "      <td>Joe Biden</td>\n",
       "      <td>Oregon</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>147</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Everything Joe Biden says about guns is idiotic.</td>\n",
       "      <td>VACountryBoy94</td>\n",
       "      <td>Virginia, USA</td>\n",
       "      <td>Joe Biden</td>\n",
       "      <td>Virginia</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     geo                                               text            user  \\\n",
       "143  NaN   \"At this point - Biden probably got 8.2 milli...  WhipperSnacker   \n",
       "144  NaN   Nicole Kidman portraying Lucille Ball...........      poppytim24   \n",
       "145  NaN  In breaking NFL Draft News, Joe Biden has chos...       stranahan   \n",
       "146  NaN   What makes Joe Biden such a good President is...   POTUS45stinks   \n",
       "147  NaN   Everything Joe Biden says about guns is idiotic.  VACountryBoy94   \n",
       "\n",
       "            location    Subject           state  \n",
       "143   South Carolina  Joe Biden  South Carolina  \n",
       "144    Missouri, USA  Joe Biden        Missouri  \n",
       "145  Sioux Falls, SD  Joe Biden    South Dakota  \n",
       "146      Oregon, USA  Joe Biden          Oregon  \n",
       "147    Virginia, USA  Joe Biden        Virginia  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tweets_biden = tweets_election_df[tweets_election_df.Subject == 'Joe Biden']\n",
    "tweets_biden.drop(tweets_biden.columns[0], axis=1, inplace = True)\n",
    "tweets_biden.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Comparison of Twitter mentions of both candidates in all US states as per data collected')"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.merge(tweets_trump['state'].value_counts(), tweets_biden['state'].value_counts(), right_index = True, \n",
    "               left_index = True)\n",
    "df = df.rename(columns = {\"state_x\": \"Total Trump Mentions\", \"state_y\": \"Total Biden Mentions\"})\n",
    "ax = df.plot(kind='barh', figsize=(16, 25), zorder=2)\n",
    "\n",
    "# Despine\n",
    "ax.spines['right'].set_visible(False)\n",
    "ax.spines['top'].set_visible(False)\n",
    "ax.spines['left'].set_visible(False)\n",
    "ax.spines['bottom'].set_visible(False)\n",
    "\n",
    "#Replacing ticks with horizontal lines\n",
    "#ax.tick_params(axis=\"both\", which=\"both\", bottom=\"off\", top=\"off\", labelbottom=\"on\", left=\"off\", right=\"off\", labelleft=\"on\")\n",
    "vals = ax.get_xticks()\n",
    "for tick in vals:\n",
    "      ax.axvline(x=tick, linestyle='dashed', alpha=0.4, color='#eeeeee', zorder=1)\n",
    "\n",
    "# Set y-axis label\n",
    "ax.set_ylabel(\"States\", labelpad=20, weight='bold', size=12)\n",
    "ax.set_title('Comparison of Twitter mentions of both candidates in all US states as per data collected',fontweight=\"bold\", size=15)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:1: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  \"\"\"Entry point for launching an IPython kernel.\n",
      "/opt/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:2: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  \n",
      "/opt/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:10: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  # Remove the CWD from sys.path while we load stuff.\n",
      "/opt/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:11: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  # This is added back by InteractiveShellApp.init_path()\n"
     ]
    },
    {
     "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>geo</th>\n",
       "      <th>text</th>\n",
       "      <th>user</th>\n",
       "      <th>location</th>\n",
       "      <th>Subject</th>\n",
       "      <th>state</th>\n",
       "      <th>sentiment</th>\n",
       "      <th>sentiment_overall</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>So Greenberg offered Stone $250k in essentia...</td>\n",
       "      <td>bchadhill</td>\n",
       "      <td>New Orleans, LA</td>\n",
       "      <td>Donald Trump</td>\n",
       "      <td>Louisiana</td>\n",
       "      <td>{'neg': 0.0, 'neu': 1.0, 'pos': 0.0, 'compound...</td>\n",
       "      <td>Neutral</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Search Warrants Move Feds Closer to Charging ...</td>\n",
       "      <td>Diana_Chumley</td>\n",
       "      <td>Tennessee, USA</td>\n",
       "      <td>Donald Trump</td>\n",
       "      <td>Tennessee</td>\n",
       "      <td>{'neg': 0.0, 'neu': 1.0, 'pos': 0.0, 'compound...</td>\n",
       "      <td>Neutral</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Propaganda media behaved, relentlessly, as if...</td>\n",
       "      <td>Desertgramma1</td>\n",
       "      <td>Arizona, USA</td>\n",
       "      <td>Donald Trump</td>\n",
       "      <td>Arizona</td>\n",
       "      <td>{'neg': 0.202, 'neu': 0.688, 'pos': 0.109, 'co...</td>\n",
       "      <td>Negative</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>This could be the lead-in every day:  Donald ...</td>\n",
       "      <td>martyq01</td>\n",
       "      <td>New Jersey</td>\n",
       "      <td>Donald Trump</td>\n",
       "      <td>New Jersey</td>\n",
       "      <td>{'neg': 0.222, 'neu': 0.778, 'pos': 0.0, 'comp...</td>\n",
       "      <td>Negative</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Donald Trump, twice impeached, criminal activ...</td>\n",
       "      <td>rradgoski</td>\n",
       "      <td>Scranton, PA</td>\n",
       "      <td>Donald Trump</td>\n",
       "      <td>Pennsylvania</td>\n",
       "      <td>{'neg': 0.349, 'neu': 0.651, 'pos': 0.0, 'comp...</td>\n",
       "      <td>Negative</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   geo                                               text           user  \\\n",
       "0  NaN    So Greenberg offered Stone $250k in essentia...      bchadhill   \n",
       "1  NaN   Search Warrants Move Feds Closer to Charging ...  Diana_Chumley   \n",
       "2  NaN   Propaganda media behaved, relentlessly, as if...  Desertgramma1   \n",
       "3  NaN   This could be the lead-in every day:  Donald ...       martyq01   \n",
       "4  NaN   Donald Trump, twice impeached, criminal activ...      rradgoski   \n",
       "\n",
       "          location       Subject         state  \\\n",
       "0  New Orleans, LA  Donald Trump     Louisiana   \n",
       "1   Tennessee, USA  Donald Trump     Tennessee   \n",
       "2     Arizona, USA  Donald Trump       Arizona   \n",
       "3       New Jersey  Donald Trump    New Jersey   \n",
       "4     Scranton, PA  Donald Trump  Pennsylvania   \n",
       "\n",
       "                                           sentiment sentiment_overall  \n",
       "0  {'neg': 0.0, 'neu': 1.0, 'pos': 0.0, 'compound...           Neutral  \n",
       "1  {'neg': 0.0, 'neu': 1.0, 'pos': 0.0, 'compound...           Neutral  \n",
       "2  {'neg': 0.202, 'neu': 0.688, 'pos': 0.109, 'co...          Negative  \n",
       "3  {'neg': 0.222, 'neu': 0.778, 'pos': 0.0, 'comp...          Negative  \n",
       "4  {'neg': 0.349, 'neu': 0.651, 'pos': 0.0, 'comp...          Negative  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tweets_trump['sentiment'] = tweets_trump['text'].apply(lambda x: sid.polarity_scores(x))\n",
    "tweets_biden['sentiment'] = tweets_biden['text'].apply(lambda x: sid.polarity_scores(x))\n",
    "def sentimentVerdict(sentiment):\n",
    "  if sentiment['compound'] >= 0.05:\n",
    "    return \"Positive\"\n",
    "  elif sentiment['compound'] <= -0.05:\n",
    "    return \"Negative\"\n",
    "  else:\n",
    "    return \"Neutral\"\n",
    "tweets_trump['sentiment_overall'] = tweets_trump['sentiment'].apply(lambda x: sentimentVerdict(x))\n",
    "tweets_biden['sentiment_overall'] = tweets_biden['sentiment'].apply(lambda x: sentimentVerdict(x))\n",
    "tweets_trump.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'TextBlob' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-10-62de07b4879e>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0mm\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mtweets_trump\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"text\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mtweets_trump\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"Subjectivity\"\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mm\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mapply\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;32mlambda\u001b[0m \u001b[0mword\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mTextBlob\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mword\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msentiment\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msubjectivity\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m/opt/anaconda3/lib/python3.7/site-packages/pandas/core/series.py\u001b[0m in \u001b[0;36mapply\u001b[0;34m(self, func, convert_dtype, args, **kwds)\u001b[0m\n\u001b[1;32m   4040\u001b[0m             \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   4041\u001b[0m                 \u001b[0mvalues\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mastype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mobject\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 4042\u001b[0;31m                 \u001b[0mmapped\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlib\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmap_infer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mf\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mconvert\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mconvert_dtype\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   4043\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   4044\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmapped\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmapped\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mSeries\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32mpandas/_libs/lib.pyx\u001b[0m in \u001b[0;36mpandas._libs.lib.map_infer\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;32m<ipython-input-10-62de07b4879e>\u001b[0m in \u001b[0;36m<lambda>\u001b[0;34m(word)\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0mm\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mtweets_trump\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"text\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mtweets_trump\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"Subjectivity\"\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mm\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mapply\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;32mlambda\u001b[0m \u001b[0mword\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mTextBlob\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mword\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msentiment\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msubjectivity\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m: name 'TextBlob' is not defined"
     ]
    }
   ],
   "source": [
    "m=tweets_trump[\"text\"]\n",
    "tweets_trump[\"Subjectivity\"] = m.apply(lambda word: TextBlob(word).sentiment.subjectivity)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Negative    110\n",
      "Neutral      98\n",
      "Positive     78\n",
      "Name: sentiment_overall, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "print(tweets_trump[\"sentiment_overall\"].value_counts())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:4: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  after removing the cwd from sys.path.\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "tweets_trump[\"sentiment_overall\"] = tweets_trump[\"sentiment_overall\"].astype(\"category\")\n",
    "\n",
    "# Visualize the Label counts\n",
    "sns.countplot(tweets_trump[\"sentiment_overall\"])\n",
    "plt.title(\"Overall Sentiment Counts for Trump\")\n",
    "plt.show()\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Negative    122\n",
      "Positive    116\n",
      "Neutral      72\n",
      "Name: sentiment_overall, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "print(tweets_biden[\"sentiment_overall\"].value_counts())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:1: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  \"\"\"Entry point for launching an IPython kernel.\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "tweets_biden[\"sentiment_overall\"] = tweets_biden[\"sentiment_overall\"].astype(\"category\")\n",
    "\n",
    "# Visualize the Label counts\n",
    "sns.countplot(tweets_biden[\"sentiment_overall\"])\n",
    "plt.title(\"Overall Sentiment Counts for Biden\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "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>Trump Positive</th>\n",
       "      <th>Trump Negative</th>\n",
       "      <th>Trump Neutral</th>\n",
       "      <th>Trump Total</th>\n",
       "      <th>Biden Positive</th>\n",
       "      <th>Biden Negative</th>\n",
       "      <th>Biden Neutral</th>\n",
       "      <th>Biden Total</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>State</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>Alabama</td>\n",
       "      <td>0</td>\n",
       "      <td>100</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>100</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Alaska</td>\n",
       "      <td>100</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>100</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Arizona</td>\n",
       "      <td>20</td>\n",
       "      <td>60</td>\n",
       "      <td>20</td>\n",
       "      <td>20</td>\n",
       "      <td>43</td>\n",
       "      <td>43</td>\n",
       "      <td>14</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Arkansas</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>California</td>\n",
       "      <td>39</td>\n",
       "      <td>22</td>\n",
       "      <td>39</td>\n",
       "      <td>36</td>\n",
       "      <td>62</td>\n",
       "      <td>31</td>\n",
       "      <td>6</td>\n",
       "      <td>32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Colorado</td>\n",
       "      <td>33</td>\n",
       "      <td>33</td>\n",
       "      <td>33</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>67</td>\n",
       "      <td>33</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Connecticut</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>100</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Delaware</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Florida</td>\n",
       "      <td>25</td>\n",
       "      <td>33</td>\n",
       "      <td>42</td>\n",
       "      <td>24</td>\n",
       "      <td>50</td>\n",
       "      <td>25</td>\n",
       "      <td>25</td>\n",
       "      <td>32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Georgia</td>\n",
       "      <td>0</td>\n",
       "      <td>100</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>33</td>\n",
       "      <td>67</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Hawaii</td>\n",
       "      <td>100</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>100</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Idaho</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Illinois</td>\n",
       "      <td>33</td>\n",
       "      <td>33</td>\n",
       "      <td>33</td>\n",
       "      <td>6</td>\n",
       "      <td>14</td>\n",
       "      <td>29</td>\n",
       "      <td>57</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Indiana</td>\n",
       "      <td>100</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>50</td>\n",
       "      <td>0</td>\n",
       "      <td>50</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Iowa</td>\n",
       "      <td>0</td>\n",
       "      <td>100</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>100</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Kansas</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Kentucky</td>\n",
       "      <td>33</td>\n",
       "      <td>33</td>\n",
       "      <td>33</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>100</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Louisiana</td>\n",
       "      <td>0</td>\n",
       "      <td>25</td>\n",
       "      <td>75</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>50</td>\n",
       "      <td>50</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Maine</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Maryland</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Massachusetts</td>\n",
       "      <td>0</td>\n",
       "      <td>100</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>67</td>\n",
       "      <td>33</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Michigan</td>\n",
       "      <td>0</td>\n",
       "      <td>62</td>\n",
       "      <td>38</td>\n",
       "      <td>16</td>\n",
       "      <td>0</td>\n",
       "      <td>67</td>\n",
       "      <td>33</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Minnesota</td>\n",
       "      <td>0</td>\n",
       "      <td>67</td>\n",
       "      <td>33</td>\n",
       "      <td>6</td>\n",
       "      <td>20</td>\n",
       "      <td>60</td>\n",
       "      <td>20</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Mississippi</td>\n",
       "      <td>0</td>\n",
       "      <td>100</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Missouri</td>\n",
       "      <td>100</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>50</td>\n",
       "      <td>50</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Montana</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Nebraska</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Nevada</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>100</td>\n",
       "      <td>2</td>\n",
       "      <td>33</td>\n",
       "      <td>50</td>\n",
       "      <td>17</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>New Hampshire</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>80</td>\n",
       "      <td>0</td>\n",
       "      <td>20</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>New Jersey</td>\n",
       "      <td>33</td>\n",
       "      <td>17</td>\n",
       "      <td>50</td>\n",
       "      <td>12</td>\n",
       "      <td>60</td>\n",
       "      <td>40</td>\n",
       "      <td>0</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>New York</td>\n",
       "      <td>0</td>\n",
       "      <td>44</td>\n",
       "      <td>56</td>\n",
       "      <td>18</td>\n",
       "      <td>40</td>\n",
       "      <td>20</td>\n",
       "      <td>40</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>New Mexico</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>North Carolina</td>\n",
       "      <td>0</td>\n",
       "      <td>67</td>\n",
       "      <td>33</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>67</td>\n",
       "      <td>33</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>North Dakota</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Ohio</td>\n",
       "      <td>100</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>67</td>\n",
       "      <td>33</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Oklahoma</td>\n",
       "      <td>40</td>\n",
       "      <td>60</td>\n",
       "      <td>0</td>\n",
       "      <td>10</td>\n",
       "      <td>100</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Oregon</td>\n",
       "      <td>50</td>\n",
       "      <td>0</td>\n",
       "      <td>50</td>\n",
       "      <td>4</td>\n",
       "      <td>25</td>\n",
       "      <td>25</td>\n",
       "      <td>50</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Pennsylvania</td>\n",
       "      <td>75</td>\n",
       "      <td>25</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>33</td>\n",
       "      <td>0</td>\n",
       "      <td>67</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Rhode Island</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>100</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>South Carolina</td>\n",
       "      <td>0</td>\n",
       "      <td>67</td>\n",
       "      <td>33</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>50</td>\n",
       "      <td>50</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>South Dakota</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>67</td>\n",
       "      <td>33</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Tennessee</td>\n",
       "      <td>0</td>\n",
       "      <td>50</td>\n",
       "      <td>50</td>\n",
       "      <td>4</td>\n",
       "      <td>20</td>\n",
       "      <td>60</td>\n",
       "      <td>20</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Texas</td>\n",
       "      <td>33</td>\n",
       "      <td>22</td>\n",
       "      <td>44</td>\n",
       "      <td>18</td>\n",
       "      <td>30</td>\n",
       "      <td>60</td>\n",
       "      <td>10</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Utah</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Vermont</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Virginia</td>\n",
       "      <td>43</td>\n",
       "      <td>14</td>\n",
       "      <td>43</td>\n",
       "      <td>14</td>\n",
       "      <td>29</td>\n",
       "      <td>43</td>\n",
       "      <td>29</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Washington</td>\n",
       "      <td>30</td>\n",
       "      <td>40</td>\n",
       "      <td>30</td>\n",
       "      <td>20</td>\n",
       "      <td>50</td>\n",
       "      <td>12</td>\n",
       "      <td>38</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>West Virginia</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Wisconsin</td>\n",
       "      <td>33</td>\n",
       "      <td>33</td>\n",
       "      <td>33</td>\n",
       "      <td>6</td>\n",
       "      <td>33</td>\n",
       "      <td>67</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Wyoming</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                Trump Positive  Trump Negative  Trump Neutral  Trump Total  \\\n",
       "State                                                                        \n",
       "Alabama                      0             100              0            4   \n",
       "Alaska                     100               0              0            2   \n",
       "Arizona                     20              60             20           20   \n",
       "Arkansas                     0               0              0            0   \n",
       "California                  39              22             39           36   \n",
       "Colorado                    33              33             33            6   \n",
       "Connecticut                  0               0              0            0   \n",
       "Delaware                     0               0              0            0   \n",
       "Florida                     25              33             42           24   \n",
       "Georgia                      0             100              0            4   \n",
       "Hawaii                     100               0              0            2   \n",
       "Idaho                        0               0              0            0   \n",
       "Illinois                    33              33             33            6   \n",
       "Indiana                    100               0              0            2   \n",
       "Iowa                         0             100              0            4   \n",
       "Kansas                       0               0              0            0   \n",
       "Kentucky                    33              33             33            6   \n",
       "Louisiana                    0              25             75            8   \n",
       "Maine                        0               0              0            0   \n",
       "Maryland                     0               0              0            0   \n",
       "Massachusetts                0             100              0            2   \n",
       "Michigan                     0              62             38           16   \n",
       "Minnesota                    0              67             33            6   \n",
       "Mississippi                  0             100              0            2   \n",
       "Missouri                   100               0              0            4   \n",
       "Montana                      0               0              0            0   \n",
       "Nebraska                     0               0              0            0   \n",
       "Nevada                       0               0            100            2   \n",
       "New Hampshire                0               0              0            0   \n",
       "New Jersey                  33              17             50           12   \n",
       "New York                     0              44             56           18   \n",
       "New Mexico                   0               0              0            0   \n",
       "North Carolina               0              67             33            6   \n",
       "North Dakota                 0               0              0            0   \n",
       "Ohio                       100               0              0            2   \n",
       "Oklahoma                    40              60              0           10   \n",
       "Oregon                      50               0             50            4   \n",
       "Pennsylvania                75              25              0            8   \n",
       "Rhode Island                 0               0            100            2   \n",
       "South Carolina               0              67             33            6   \n",
       "South Dakota                 0               0              0            0   \n",
       "Tennessee                    0              50             50            4   \n",
       "Texas                       33              22             44           18   \n",
       "Utah                         0               0              0            0   \n",
       "Vermont                      0               0              0            0   \n",
       "Virginia                    43              14             43           14   \n",
       "Washington                  30              40             30           20   \n",
       "West Virginia                0               0              0            0   \n",
       "Wisconsin                   33              33             33            6   \n",
       "Wyoming                      0               0              0            0   \n",
       "\n",
       "                Biden Positive  Biden Negative  Biden Neutral  Biden Total  \n",
       "State                                                                       \n",
       "Alabama                    100               0              0            2  \n",
       "Alaska                     100               0              0            2  \n",
       "Arizona                     43              43             14           14  \n",
       "Arkansas                     0               0              0            0  \n",
       "California                  62              31              6           32  \n",
       "Colorado                     0              67             33            6  \n",
       "Connecticut                  0             100              0            2  \n",
       "Delaware                     0               0              0            0  \n",
       "Florida                     50              25             25           32  \n",
       "Georgia                     33              67              0            6  \n",
       "Hawaii                       0             100              0            2  \n",
       "Idaho                        0               0              0            0  \n",
       "Illinois                    14              29             57           14  \n",
       "Indiana                     50               0             50            4  \n",
       "Iowa                         0             100              0            4  \n",
       "Kansas                       0               0              0            0  \n",
       "Kentucky                     0             100              0            4  \n",
       "Louisiana                    0              50             50            4  \n",
       "Maine                        0               0              0            0  \n",
       "Maryland                     0               0              0            0  \n",
       "Massachusetts               67              33              0            6  \n",
       "Michigan                     0              67             33            6  \n",
       "Minnesota                   20              60             20           10  \n",
       "Mississippi                  0               0              0            0  \n",
       "Missouri                     0              50             50            4  \n",
       "Montana                      0               0              0            0  \n",
       "Nebraska                     0               0              0            0  \n",
       "Nevada                      33              50             17           12  \n",
       "New Hampshire               80               0             20           10  \n",
       "New Jersey                  60              40              0           10  \n",
       "New York                    40              20             40           20  \n",
       "New Mexico                   0               0              0            0  \n",
       "North Carolina               0              67             33            6  \n",
       "North Dakota                 0               0              0            0  \n",
       "Ohio                        67              33              0            6  \n",
       "Oklahoma                   100               0              0            2  \n",
       "Oregon                      25              25             50            8  \n",
       "Pennsylvania                33               0             67            6  \n",
       "Rhode Island                 0               0              0            0  \n",
       "South Carolina               0              50             50            4  \n",
       "South Dakota                 0              67             33            6  \n",
       "Tennessee                   20              60             20           10  \n",
       "Texas                       30              60             10           20  \n",
       "Utah                         0               0              0            0  \n",
       "Vermont                      0               0              0            0  \n",
       "Virginia                    29              43             29           14  \n",
       "Washington                  50              12             38           16  \n",
       "West Virginia                0               0              0            0  \n",
       "Wisconsin                   33              67              0            6  \n",
       "Wyoming                      0               0              0            0  "
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tweets_trump_location = tweets_trump.groupby(['state', 'sentiment_overall']).count()\n",
    "tweets_trump_location = tweets_trump_location['user']\n",
    "tweets_biden_location = tweets_biden.groupby(['state', 'sentiment_overall']).count()\n",
    "tweets_biden_location = tweets_biden_location['user']\n",
    "tweets_location_df = pd.DataFrame({'State': [state for state in states],\n",
    "        'Trump Positive': [0 for state in states],\n",
    "        'Trump Negative': [0 for state in states],\n",
    "        'Trump Neutral': [0 for state in states],\n",
    "        'Trump Total': [0 for state in states],\n",
    "        'Biden Positive': [0 for state in states],\n",
    "        'Biden Negative': [0 for state in states],\n",
    "        'Biden Neutral': [0 for state in states],\n",
    "        'Biden Total': [0 for state in states]})\n",
    "tweets_location_df.set_index('State', inplace = True)\n",
    "for state in states:\n",
    "  positiveTrump, negativeTrump, neutralTrump, positiveBiden, negativeBiden, neutralBiden = 0, 0, 0, 0, 0, 0\n",
    "  try:\n",
    "    positiveTrump = tweets_trump_location[state]['Positive']\n",
    "  except:\n",
    "    positiveTrump = 0\n",
    "  \n",
    "  try:\n",
    "    negativeTrump = tweets_trump_location[state]['Negative']\n",
    "  except:\n",
    "    negativeTrump = 0\n",
    "  \n",
    "  try:\n",
    "    neutralTrump = tweets_trump_location[state]['Neutral']\n",
    "  except:\n",
    "    neutralTrump = 0\n",
    "  \n",
    "  try:\n",
    "    positiveBiden = tweets_biden_location[state]['Positive']\n",
    "  except:\n",
    "    positiveBiden = 0\n",
    "  \n",
    "  try:\n",
    "    negativeBiden = tweets_biden_location[state]['Negative']\n",
    "  except:\n",
    "    negativeBiden = 0\n",
    "  \n",
    "  try:\n",
    "    neutralBiden = tweets_biden_location[state]['Neutral']\n",
    "  except:\n",
    "    neutralBiden = 0\n",
    "\n",
    "  totalTrump = positiveTrump + negativeTrump + neutralTrump\n",
    "  totalBiden = positiveBiden + negativeBiden + neutralBiden\n",
    "\n",
    "  if totalTrump == 0:\n",
    "    tweets_location_df.at[state, 'Trump Positive'], tweets_location_df.at[state, 'Trump Negative'], tweets_location_df.at[state, 'Trump Neutral'] = 0,0,0\n",
    "  else:\n",
    "    tweets_location_df.at[state, 'Trump Positive'] = round((positiveTrump/totalTrump)*100.0)\n",
    "    tweets_location_df.at[state, 'Trump Negative'] = round((negativeTrump/totalTrump)*100.0)\n",
    "    tweets_location_df.at[state, 'Trump Neutral'] = round((neutralTrump/totalTrump)*100.0)\n",
    "  tweets_location_df.at[state, 'Trump Total'] = totalTrump\n",
    "  \n",
    "  if totalBiden == 0:\n",
    "    tweets_location_df.at[state, 'Biden Positive'], tweets_location_df.at[state, 'Biden Negative'], tweets_location_df.at[state, 'Biden Neutral'] = 0,0,0\n",
    "  else:\n",
    "    tweets_location_df.at[state, 'Biden Positive'] = round((positiveBiden/totalBiden)*100.0)\n",
    "    tweets_location_df.at[state, 'Biden Negative'] = round((negativeBiden/totalBiden)*100.0)\n",
    "    tweets_location_df.at[state, 'Biden Neutral'] = round((neutralBiden/totalBiden)*100.0)\n",
    "  tweets_location_df.at[state, 'Biden Total'] = totalBiden\n",
    "tweets_location_df\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "le = LabelEncoder()\n",
    "tweets_trump[\"Label_enc\"] = le.fit_transform(tweets_processed[\"Label\"])\n",
    "\n",
    "# Display the encoded labels\n",
    "display(tweets_processed[[\"Label_enc\"]].head())\n",
    "\n",
    "# Select the features and the target\n",
    "X = tweets_processed['Processed']\n",
    "y = tweets_processed[\"Label_enc\"]\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=34, stratify=y)\n",
    "\n"
   ]
  }
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