Emotion_Test.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Emotion Analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Import Dependencies"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import nltk\n",
    "import string\n",
    "\n",
    "from collections import Counter\n",
    "\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Reading CSV File"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "first_df = pd.read_csv(\"Concat_ML_Data/first_debate.csv\")\n",
    "second_df = pd.read_csv(\"Concat_ML_Data/second_debate.csv\")\n",
    "third_df = pd.read_csv(\"Concat_ML_Data/third_debate.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>tweet</th>\n",
       "      <th>replies_count</th>\n",
       "      <th>retweets_count</th>\n",
       "      <th>likes_count</th>\n",
       "      <th>candidate</th>\n",
       "      <th>tweet_length</th>\n",
       "      <th>Subjectivity</th>\n",
       "      <th>Polarity</th>\n",
       "      <th>Sentiment</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Should we have CRT in schools We need leader</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>Biden</td>\n",
       "      <td>9</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>Neutral</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Literally trump but ok</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Biden</td>\n",
       "      <td>4</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>Positive</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Pres you missed the op to mention HBCU obvious...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Biden</td>\n",
       "      <td>25</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>-0.111111</td>\n",
       "      <td>Negative</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>THIS IS ANTIFA</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Biden</td>\n",
       "      <td>3</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>Neutral</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Yeah silence is oppression especially from th...</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Biden</td>\n",
       "      <td>45</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>Neutral</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                               tweet  replies_count  \\\n",
       "0      Should we have CRT in schools We need leader               1   \n",
       "1                             Literally trump but ok              0   \n",
       "2  Pres you missed the op to mention HBCU obvious...              0   \n",
       "3                                     THIS IS ANTIFA              0   \n",
       "4   Yeah silence is oppression especially from th...              2   \n",
       "\n",
       "   retweets_count  likes_count candidate  tweet_length  Subjectivity  \\\n",
       "0               0            2     Biden             9      0.000000   \n",
       "1               0            0     Biden             4      0.500000   \n",
       "2               0            0     Biden            25      0.333333   \n",
       "3               0            0     Biden             3      0.000000   \n",
       "4               0            0     Biden            45      1.000000   \n",
       "\n",
       "   Polarity Sentiment  \n",
       "0  0.000000   Neutral  \n",
       "1  0.500000  Positive  \n",
       "2 -0.111111  Negative  \n",
       "3  0.000000   Neutral  \n",
       "4  0.000000   Neutral  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_df=pd.concat([first_df, second_df, third_df])\n",
    "test_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Separating entire dataset to for Trump and Biden\n",
    "biden_sentiment=test_df[(test_df['candidate']=='Biden')]\n",
    "trump_sentiment=test_df[(test_df['candidate']=='Trump')]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Keeping the tweet columns for both datasets\n",
    "biden_sentiment=pd.DataFrame(biden_sentiment, columns=['tweet'])\n",
    "trump_sentiment=pd.DataFrame(trump_sentiment, columns=['tweet'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Pre-processing for Emotion Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Converting all text to lowercase\n",
    "trump_sentiment['tweet']=trump_sentiment['tweet'].str.lower()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Creating Function to tokenize words in tweet columns\n",
    "def tokenized_words(row):\n",
    "    tweet = row['tweet']\n",
    "    tokens = nltk.word_tokenize(tweet)\n",
    "    token_wds = [word for word in tokens if word.isalpha()]\n",
    "    return token_wds"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>tweet</th>\n",
       "      <th>t_words</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>490063</th>\n",
       "      <td>donald keep up your infantile name calling it...</td>\n",
       "      <td>[donald, keep, up, your, infantile, name, call...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>490064</th>\n",
       "      <td>the sad thing is he probably proud of them wa...</td>\n",
       "      <td>[the, sad, thing, is, he, probably, proud, of,...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>490065</th>\n",
       "      <td>m just going off this really don give much of...</td>\n",
       "      <td>[m, just, going, off, this, really, don, give,...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>490066</th>\n",
       "      <td>holy cow 25 people at bidens campaign stop yo...</td>\n",
       "      <td>[holy, cow, people, at, bidens, campaign, stop...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>490067</th>\n",
       "      <td>maybe come to the next debate and show some r...</td>\n",
       "      <td>[maybe, come, to, the, next, debate, and, show...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                    tweet  \\\n",
       "490063   donald keep up your infantile name calling it...   \n",
       "490064   the sad thing is he probably proud of them wa...   \n",
       "490065   m just going off this really don give much of...   \n",
       "490066   holy cow 25 people at bidens campaign stop yo...   \n",
       "490067   maybe come to the next debate and show some r...   \n",
       "\n",
       "                                                  t_words  \n",
       "490063  [donald, keep, up, your, infantile, name, call...  \n",
       "490064  [the, sad, thing, is, he, probably, proud, of,...  \n",
       "490065  [m, just, going, off, this, really, don, give,...  \n",
       "490066  [holy, cow, people, at, bidens, campaign, stop...  \n",
       "490067  [maybe, come, to, the, next, debate, and, show...  "
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Applying tokeninzed_words function to dataset\n",
    "trump_sentiment['t_words']=trump_sentiment.apply(tokenized_words, axis=1)\n",
    "trump_sentiment.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Import Stopwords from NLTK"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "from nltk.corpus import stopwords\n",
    "stops = set(stopwords.words(\"english\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Creating Function to remove stop words in tweet column\n",
    "def remove_stop_wds(row):\n",
    "    t_words=row['t_words']\n",
    "    meaningful_words = [word for word in t_words if not word in stops]\n",
    "    return (meaningful_words)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>tweet</th>\n",
       "      <th>t_words</th>\n",
       "      <th>final_wds</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>490063</th>\n",
       "      <td>donald keep up your infantile name calling it...</td>\n",
       "      <td>[donald, keep, up, your, infantile, name, call...</td>\n",
       "      <td>[donald, keep, infantile, name, calling, worki...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>490064</th>\n",
       "      <td>the sad thing is he probably proud of them wa...</td>\n",
       "      <td>[the, sad, thing, is, he, probably, proud, of,...</td>\n",
       "      <td>[sad, thing, probably, proud, wait, sorry, thi...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>490065</th>\n",
       "      <td>m just going off this really don give much of...</td>\n",
       "      <td>[m, just, going, off, this, really, don, give,...</td>\n",
       "      <td>[going, really, give, much, shit, probably, qu...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>490066</th>\n",
       "      <td>holy cow 25 people at bidens campaign stop yo...</td>\n",
       "      <td>[holy, cow, people, at, bidens, campaign, stop...</td>\n",
       "      <td>[holy, cow, people, bidens, campaign, stop, je...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>490067</th>\n",
       "      <td>maybe come to the next debate and show some r...</td>\n",
       "      <td>[maybe, come, to, the, next, debate, and, show...</td>\n",
       "      <td>[maybe, come, next, debate, show, respect, sou...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                    tweet  \\\n",
       "490063   donald keep up your infantile name calling it...   \n",
       "490064   the sad thing is he probably proud of them wa...   \n",
       "490065   m just going off this really don give much of...   \n",
       "490066   holy cow 25 people at bidens campaign stop yo...   \n",
       "490067   maybe come to the next debate and show some r...   \n",
       "\n",
       "                                                  t_words  \\\n",
       "490063  [donald, keep, up, your, infantile, name, call...   \n",
       "490064  [the, sad, thing, is, he, probably, proud, of,...   \n",
       "490065  [m, just, going, off, this, really, don, give,...   \n",
       "490066  [holy, cow, people, at, bidens, campaign, stop...   \n",
       "490067  [maybe, come, to, the, next, debate, and, show...   \n",
       "\n",
       "                                                final_wds  \n",
       "490063  [donald, keep, infantile, name, calling, worki...  \n",
       "490064  [sad, thing, probably, proud, wait, sorry, thi...  \n",
       "490065  [going, really, give, much, shit, probably, qu...  \n",
       "490066  [holy, cow, people, bidens, campaign, stop, je...  \n",
       "490067  [maybe, come, next, debate, show, respect, sou...  "
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Applying remove_stop_wds function to dataset\n",
    "trump_sentiment['final_wds']=trump_sentiment.apply(remove_stop_wds, axis=1)\n",
    "trump_sentiment.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Creating list of all meaningful words in the final_wds column\n",
    "meaningful_list =[]\n",
    "def meaningful_words(row):\n",
    "    final_words=row['final_wds']\n",
    "    for word in final_words:\n",
    "        meaningful_list.append(word)\n",
    "    return ('Complete')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "490063    Complete\n",
       "490064    Complete\n",
       "490065    Complete\n",
       "490066    Complete\n",
       "490067    Complete\n",
       "            ...   \n",
       "851654    Complete\n",
       "851655    Complete\n",
       "851656    Complete\n",
       "851657    Complete\n",
       "851658    Complete\n",
       "Length: 2035571, dtype: object"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Applying meaningful_words function to get list of final words\n",
    "trump_sentiment.apply(meaningful_words, axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Performing Emotion Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Counter({' sad': 58, ' happy': 58, ' fearful': 40, ' angry': 31, ' attracted': 30, ' powerless': 21, ' singled out': 15, ' hated': 14, ' anxious': 14, ' attached': 13, ' fearless': 13, ' cheated': 12, ' entitled': 12, ' belittled': 10, ' esteemed': 9, ' adequate': 9, ' loved': 8, ' apathetic': 8, ' alone': 8, ' lustful': 7, ' surprise': 6, ' bored': 5, ' independent': 5, ' codependent': 5, ' demoralized': 5, ' free': 5, ' derailed': 4, ' embarrassed': 3, ' average': 3, ' obsessed': 3, ' focused': 3, ' ecstatic': 3, ' lost': 3, ' safe': 2, ' burdened': 2})\n"
     ]
    }
   ],
   "source": [
    "emotion_list = []\n",
    "with open('emotions.txt', 'r') as file:\n",
    "    for line in file:\n",
    "        clear_line = line.replace('\\n', '').replace(',', '').replace(\"'\", '').strip()\n",
    "        word, emotion = clear_line.split(':')\n",
    "        if word in meaningful_list:\n",
    "            emotion_list.append(emotion)\n",
    "\n",
    "w = Counter(emotion_list)\n",
    "print(w)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Creating lists from keys and values for w\n",
    "x=list(w.keys())\n",
    "y=list(w.values())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Emotion</th>\n",
       "      <th>Value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>cheated</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>singled out</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>loved</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>attracted</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>sad</td>\n",
       "      <td>58</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>fearful</td>\n",
       "      <td>40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>happy</td>\n",
       "      <td>58</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>angry</td>\n",
       "      <td>31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>bored</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>esteemed</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>lustful</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>attached</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>independent</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>embarrassed</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>surprise</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>powerless</td>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>fearless</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>safe</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>adequate</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>hated</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>codependent</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>average</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>apathetic</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>obsessed</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>entitled</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>belittled</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>alone</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>focused</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>demoralized</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>derailed</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>anxious</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>ecstatic</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>free</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>lost</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>burdened</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Emotion  Value\n",
       "0        cheated     12\n",
       "1    singled out     15\n",
       "2          loved      8\n",
       "3      attracted     30\n",
       "4            sad     58\n",
       "5        fearful     40\n",
       "6          happy     58\n",
       "7          angry     31\n",
       "8          bored      5\n",
       "9       esteemed      9\n",
       "10       lustful      7\n",
       "11      attached     13\n",
       "12   independent      5\n",
       "13   embarrassed      3\n",
       "14      surprise      6\n",
       "15     powerless     21\n",
       "16      fearless     13\n",
       "17          safe      2\n",
       "18      adequate      9\n",
       "19         hated     14\n",
       "20   codependent      5\n",
       "21       average      3\n",
       "22     apathetic      8\n",
       "23      obsessed      3\n",
       "24      entitled     12\n",
       "25     belittled     10\n",
       "26         alone      8\n",
       "27       focused      3\n",
       "28   demoralized      5\n",
       "29      derailed      4\n",
       "30       anxious     14\n",
       "31      ecstatic      3\n",
       "32          free      5\n",
       "33          lost      3\n",
       "34      burdened      2"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Creating pandas dataframe from x and y lists\n",
    "emotion_df = pd.DataFrame({\n",
    "          \"Emotion\" : x,\n",
    "          \"Value\": y})\n",
    "\n",
    "emotion_df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Plotting Barplot for Emotions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x648 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax1 = plt.subplots(figsize=(16,9))\n",
    "ax1.barh(emotion_df.Emotion, emotion_df.Value, color=['red'])\n",
    "ax1.set_xlabel('Number', size=15)\n",
    "ax1.set_ylabel('Emotions', size=15)\n",
    "ax1.set_title('Emotion Analysis - Donald Trump')\n",
    "# fig.autofmt_xdate()\n",
    "plt.savefig('Images/Trump_2020_emotions.png')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<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>Emotion</th>\n",
       "      <th>Value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>sad</td>\n",
       "      <td>58</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>happy</td>\n",
       "      <td>58</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>fearful</td>\n",
       "      <td>40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>angry</td>\n",
       "      <td>31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>attracted</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>powerless</td>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>singled out</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>anxious</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>hated</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>fearless</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>attached</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>entitled</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>cheated</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>belittled</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>adequate</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>esteemed</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>loved</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>apathetic</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>alone</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>lustful</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>surprise</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>free</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>demoralized</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>independent</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>codependent</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>bored</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>derailed</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>ecstatic</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>embarrassed</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>focused</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>average</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>lost</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>obsessed</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>safe</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>burdened</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Emotion  Value\n",
       "4            sad     58\n",
       "6          happy     58\n",
       "5        fearful     40\n",
       "7          angry     31\n",
       "3      attracted     30\n",
       "15     powerless     21\n",
       "1    singled out     15\n",
       "30       anxious     14\n",
       "19         hated     14\n",
       "16      fearless     13\n",
       "11      attached     13\n",
       "24      entitled     12\n",
       "0        cheated     12\n",
       "25     belittled     10\n",
       "18      adequate      9\n",
       "9       esteemed      9\n",
       "2          loved      8\n",
       "22     apathetic      8\n",
       "26         alone      8\n",
       "10       lustful      7\n",
       "14      surprise      6\n",
       "32          free      5\n",
       "28   demoralized      5\n",
       "12   independent      5\n",
       "20   codependent      5\n",
       "8          bored      5\n",
       "29      derailed      4\n",
       "31      ecstatic      3\n",
       "13   embarrassed      3\n",
       "27       focused      3\n",
       "21       average      3\n",
       "33          lost      3\n",
       "23      obsessed      3\n",
       "17          safe      2\n",
       "34      burdened      2"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Arranging the emotions by descending values\n",
    "top_emotions_trump = emotion_df.sort_values([\"Value\"], ascending=False)\n",
    "top_emotions_trump"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "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>Emotion</th>\n",
       "      <th>Value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>sad</td>\n",
       "      <td>58</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>happy</td>\n",
       "      <td>58</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>fearful</td>\n",
       "      <td>40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>angry</td>\n",
       "      <td>31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>attracted</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>powerless</td>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>singled out</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>anxious</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>hated</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>fearless</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Emotion  Value\n",
       "4            sad     58\n",
       "6          happy     58\n",
       "5        fearful     40\n",
       "7          angry     31\n",
       "3      attracted     30\n",
       "15     powerless     21\n",
       "1    singled out     15\n",
       "30       anxious     14\n",
       "19         hated     14\n",
       "16      fearless     13"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Creating table with top 10 Emotions\n",
    "top10_emotions_trump = top_emotions_trump.head(10)\n",
    "top10_emotions_trump"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,6))\n",
    "\n",
    "plt.barh('Emotion', 'Value',data=top10_emotions_trump, color=['red'])\n",
    "plt.xlabel(\"Number\", size=15)\n",
    "plt.ylabel(\"Emotion\", size=15)\n",
    "plt.title(\"Trump Top 10 Emotions 2020\", size=18)\n",
    "plt.gca().invert_yaxis()\n",
    "\n",
    "plt.savefig(\"Images/Trump_2020_top10_emotions.png\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "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>Emotion</th>\n",
       "      <th>Value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>bored</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>derailed</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>ecstatic</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>embarrassed</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>focused</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>average</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>lost</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>obsessed</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>safe</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>burdened</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Emotion  Value\n",
       "8          bored      5\n",
       "29      derailed      4\n",
       "31      ecstatic      3\n",
       "13   embarrassed      3\n",
       "27       focused      3\n",
       "21       average      3\n",
       "33          lost      3\n",
       "23      obsessed      3\n",
       "17          safe      2\n",
       "34      burdened      2"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Bottom 10 Emotions\n",
    "bottom10_emotions_trump = top_emotions_trump.tail(10)\n",
    "bottom10_emotions_trump"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,6))\n",
    "\n",
    "plt.barh('Emotion', 'Value',data=bottom10_emotions_trump, color=['red'])\n",
    "plt.xlabel(\"Number\", size=15)\n",
    "plt.ylabel(\"Emotion\", size=15)\n",
    "plt.title(\"Trump Bottom 10 Emotions 2020\", size=18)\n",
    "plt.gca().invert_yaxis()\n",
    "plt.savefig(\"Images/Trump_2020_bottom10_emotions.png\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "PythonData",
   "language": "python",
   "name": "pythondata"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}