Twitter Sentiment Analysis.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Predicting US Elections Using Sentiment Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Importing essential libraries\n",
    "import pandas as pd \n",
    "import numpy as np \n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "from textblob import TextBlob\n",
    "from wordcloud import WordCloud\n",
    "import plotly.graph_objects as go \n",
    "import plotly.express as px"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Importing reviews\n",
    "trump_reviews= pd.read_csv('Trumpall2.csv')\n",
    "biden_reviews= pd.read_csv(\"Bidenall2.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(              user                                               text\n",
       " 0      manny_rosen   @sanofi please tell us how many shares the Cr...\n",
       " 1        osi_abdul   https://t.co/atM98CpqF7  Like, comment, RT #P...\n",
       " 2          Patsyrw   Your AG Barr is as useless &amp; corrupt as y...\n",
       " 3  seyedebrahimi_m   Mr. Trump! Wake Up!  Most of the comments bel...\n",
       " 4    James09254677   After 4 years you think you would have figure...,\n",
       "            user                                               text\n",
       " 0   MarkHodder3    @JoeBiden And we’ll find out who won in 2026...\n",
       " 1    K87327961G  @JoeBiden Your Democratic Nazi Party cannot be...\n",
       " 2      OldlaceA                        @JoeBiden So did Lying Barr\n",
       " 3    penblogger  @JoeBiden It's clear you didnt compose this tw...\n",
       " 4  Aquarian0264         @JoeBiden I will vote in person thank you.)"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Getting a glimpse of the data\n",
    "trump_reviews.head(), biden_reviews.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Sentiment Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Trump : Sentiment(polarity=0.15, subjectivity=0.3125)\n",
      "Biden : Sentiment(polarity=0.6, subjectivity=0.9)\n"
     ]
    }
   ],
   "source": [
    "''' \n",
    "Using TextBlob library to perform text classification in either positive or negative\n",
    "'''\n",
    "textblob1= TextBlob(trump_reviews[\"text\"][10])\n",
    "print(\"Trump :\", textblob1.sentiment)\n",
    "\n",
    "textblob2= TextBlob(biden_reviews[\"text\"][500])\n",
    "print(\"Biden :\", textblob2.sentiment)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                 user                                               text  \\\n",
      "2783          4diva63  @realDonaldTrump For the 1/100 time, absentee ...   \n",
      "2784         hidge826  @realDonaldTrump If you’re so scared of losing...   \n",
      "2785     SpencerRossy  @realDonaldTrump I rarely get involved with fo...   \n",
      "2786  ScoobyMcpherson  @realDonaldTrump This is the moment when Trump...   \n",
      "2787          bjklinz     @realDonaldTrump I’m sorry, Donald. No. #POTUS   \n",
      "\n",
      "      Sentiment Polarity  \n",
      "2783               0.000  \n",
      "2784               0.000  \n",
      "2785               0.225  \n",
      "2786               0.000  \n",
      "2787              -0.500  \n",
      "             user                                               text  \\\n",
      "2535    meryn1977  @JoeBiden You'll just try to calm those waters...   \n",
      "2536  BSNelson114  @JoeBiden 96 days 96 dias #VoteJoeBiden2020  #...   \n",
      "2537     KenCapel  @JoeBiden YOU THINK YOU CAN DO THAT??? YOU CAN...   \n",
      "2538   LeslyeHale  @JoeBiden Trump wants our children back at sch...   \n",
      "2539     rerickre  @JoeBiden ... and I know, because it’s much co...   \n",
      "\n",
      "      Sentiment Polarity  \n",
      "2535                0.15  \n",
      "2536                0.00  \n",
      "2537                0.00  \n",
      "2538                0.10  \n",
      "2539                0.20  \n"
     ]
    }
   ],
   "source": [
    "'''\n",
    "Polarity ranges from -1 to +1(negative to positive) and tells whether the text has negative sentiments or positive sentiments. Polarity tells about factual information.\n",
    "'''\n",
    "def find_pol(review):\n",
    "    return TextBlob(review).sentiment.polarity\n",
    "trump_reviews[\"Sentiment Polarity\"]= trump_reviews[\"text\"].apply(find_pol)\n",
    "print(trump_reviews.tail())\n",
    "\n",
    "biden_reviews[\"Sentiment Polarity\"]= biden_reviews[\"text\"].apply(find_pol)\n",
    "print(biden_reviews.tail())"
   ]
  },
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   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                 user                                               text  \\\n",
      "2783          4diva63  @realDonaldTrump For the 1/100 time, absentee ...   \n",
      "2784         hidge826  @realDonaldTrump If you’re so scared of losing...   \n",
      "2785     SpencerRossy  @realDonaldTrump I rarely get involved with fo...   \n",
      "2786  ScoobyMcpherson  @realDonaldTrump This is the moment when Trump...   \n",
      "2787          bjklinz     @realDonaldTrump I’m sorry, Donald. No. #POTUS   \n",
      "\n",
      "      Sentiment Polarity Expression_Label  \n",
      "2783               0.000          Neutral  \n",
      "2784               0.000          Neutral  \n",
      "2785               0.225         positive  \n",
      "2786               0.000          Neutral  \n",
      "2787              -0.500         negative  \n",
      "             user                                               text  \\\n",
      "2535    meryn1977  @JoeBiden You'll just try to calm those waters...   \n",
      "2536  BSNelson114  @JoeBiden 96 days 96 dias #VoteJoeBiden2020  #...   \n",
      "2537     KenCapel  @JoeBiden YOU THINK YOU CAN DO THAT??? YOU CAN...   \n",
      "2538   LeslyeHale  @JoeBiden Trump wants our children back at sch...   \n",
      "2539     rerickre  @JoeBiden ... and I know, because it’s much co...   \n",
      "\n",
      "      Sentiment Polarity Expression_Label  \n",
      "2535                0.15         positive  \n",
      "2536                0.00          Neutral  \n",
      "2537                0.00          Neutral  \n",
      "2538                0.10         positive  \n",
      "2539                0.20         positive  \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-33-dcf258845511>:3: SettingWithCopyWarning:\n",
      "\n",
      "\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: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "\n",
      "<ipython-input-33-dcf258845511>:8: SettingWithCopyWarning:\n",
      "\n",
      "\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: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "\n"
     ]
    }
   ],
   "source": [
    "''' Adding a new attribute Expression Label that will classify the text as positive or negative on the basis of polarity'''\n",
    "trump_reviews['Expression_Label']= np.where(trump_reviews['Sentiment Polarity']>0, 'positive', 'negative')\n",
    "trump_reviews[\"Expression_Label\"][trump_reviews[\"Sentiment Polarity\"]==0]=\"Neutral\"\n",
    "print(trump_reviews.tail())\n",
    "\n",
    "\n",
    "biden_reviews['Expression_Label']= np.where(biden_reviews['Sentiment Polarity']>0, 'positive', 'negative')\n",
    "biden_reviews[\"Expression_Label\"][biden_reviews[\"Sentiment Polarity\"]==0]=\"Neutral\"\n",
    "print(biden_reviews.tail())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1464, 4)\n",
      "(1324, 4)\n",
      "(1509, 4)\n",
      "(1031, 4)\n"
     ]
    }
   ],
   "source": [
    "# Droping all the tweets marked with neutral polarity from both the datastes to balance the data.\n",
    "reviews1= trump_reviews[trump_reviews['Sentiment Polarity']==0.00000]\n",
    "print(reviews1.shape)\n",
    "\n",
    "cond1=trump_reviews['Sentiment Polarity'].isin(reviews1['Sentiment Polarity'])\n",
    "trump_reviews.drop(trump_reviews[cond1].index, inplace = True)\n",
    "print(trump_reviews.shape)\n",
    "\n",
    "\n",
    "reviews2= biden_reviews[biden_reviews['Sentiment Polarity']==0.00000]\n",
    "print(reviews2.shape)\n",
    "\n",
    "cond2=biden_reviews['Sentiment Polarity'].isin(reviews2['Sentiment Polarity'])\n",
    "biden_reviews.drop(biden_reviews[cond2].index, inplace = True)\n",
    "print(biden_reviews.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1000, 4)\n",
      "(1000, 4)\n"
     ]
    }
   ],
   "source": [
    "# Balancing the dataset\n",
    "# Donald Trump\n",
    "np.random.seed(10)\n",
    "remove_n =324\n",
    "drop_indices = np.random.choice(trump_reviews.index, remove_n, replace=False)\n",
    "df_subset_trump = trump_reviews.drop(drop_indices)\n",
    "print(df_subset_trump.shape)\n",
    "# Joe Biden\n",
    "np.random.seed(10)\n",
    "remove_n =31\n",
    "drop_indices = np.random.choice(biden_reviews.index, remove_n, replace=False)\n",
    "df_subset_biden = biden_reviews.drop(drop_indices)\n",
    "print(df_subset_biden.shape)"
   ]
  },
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   "cell_type": "code",
   "execution_count": 38,
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     "name": "stdout",
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      "                  user  text  Sentiment Polarity\n",
      "Expression_Label                                \n",
      "negative           449   449                 449\n",
      "positive           551   551                 551\n",
      "                  user  text  Sentiment Polarity\n",
      "Expression_Label                                \n",
      "negative           393   393                 393\n",
      "positive           607   607                 607\n"
     ]
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    "#### Analysing the data for positive and negative sentiments\n",
    "count_1 = df_subset_trump.groupby('Expression_Label').count()\n",
    "print(count_1)\n",
    "\n",
    "negative_per1 = (count_1['Sentiment Polarity'][0]/1000)*10\n",
    "positive_per1 = (count_1['Sentiment Polarity'][1]/1000)*100\n",
    "\n",
    "count_2 = df_subset_biden.groupby('Expression_Label').count()\n",
    "print(count_2)\n",
    "\n",
    "negative_per2 = (count_2['Sentiment Polarity'][0]/1000)*100\n",
    "positive_per2 = (count_2['Sentiment Polarity'][1]/1000)*100\n",
    "\n",
    "Politicians = ['Joe Biden', 'Donald Trump']\n",
    "lis_pos = [positive_per1, positive_per2]\n",
    "lis_neg = [negative_per1, negative_per2]\n",
    "\n",
    "fig = go.Figure(data=[\n",
    "    go.Bar(name='Positive', x=Politicians, y=lis_pos),\n",
    "    go.Bar(name='Negative', x=Politicians, y=lis_neg)\n",
    "])\n",
    "# Change the bar mode\n",
    "fig.update_layout(barmode='group')\n",
    "fig.show()"
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