Text_emotion_detection (1).ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "ed98bc2d-477b-4e91-ba5d-1e471e7b131d",
   "metadata": {},
   "source": [
    "# Credits: Data Used for the Project\n",
    "+ https://github.com/lukasgarbas/nlp-text-emotion/blob/master/data/data_train.csv\n",
    "+ https://www.youtube.com/watch?v=t1TkAcSDsI8&t=2579s (Practice Tutorial)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "1ded3c66-c6fd-437b-b14d-4632e6edb50d",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "0800de6d-5d03-4b2f-9534-92d7cf1149d4",
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "65587ec2-db24-4dc7-8720-9d6623ab569a",
   "metadata": {},
   "outputs": [],
   "source": [
    "import neattext.functions as nfx"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "92f9c733-12ff-4391-b2d5-ad08cfd4e693",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv(\"https://raw.githubusercontent.com/lukasgarbas/nlp-text-emotion/master/data/data_test.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "32e2e110-ff6c-4cbf-8608-5ef125cbd240",
   "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>Text</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>sadness</td>\n",
       "      <td>I experienced this emotion when my grandfather...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>neutral</td>\n",
       "      <td>when I first moved in , I walked everywhere ....</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>anger</td>\n",
       "      <td>` Oh ! \" she bleated , her voice high and rath...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>fear</td>\n",
       "      <td>However , does the right hon. Gentleman recogn...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>sadness</td>\n",
       "      <td>My boyfriend didn't turn up after promising th...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Emotion                                               Text\n",
       "0  sadness  I experienced this emotion when my grandfather...\n",
       "1  neutral   when I first moved in , I walked everywhere ....\n",
       "2    anger  ` Oh ! \" she bleated , her voice high and rath...\n",
       "3     fear  However , does the right hon. Gentleman recogn...\n",
       "4  sadness  My boyfriend didn't turn up after promising th..."
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "51cdc929-672f-4f79-85ab-8fa23ae67409",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(3393, 2)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "ba6d2097-32d4-49f2-a4c6-2a789b1780e6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Emotion    object\n",
       "Text       object\n",
       "dtype: object"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "38e675b6-020d-41d3-87b5-4d36849b3263",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Emotion    0\n",
       "Text       0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "29606fb1-588f-4237-ae5c-d8b1767319fa",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "df['Emotion'].value_counts().plot(kind=\"bar\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "61b9e8fc-17e5-4753-8cc4-d35d08666235",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Emotion', ylabel='count'>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "sns.countplot(x=\"Emotion\", data=df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "4b6a6a84-7a03-4810-b991-028378d8c530",
   "metadata": {},
   "outputs": [],
   "source": [
    "#sentiment analysis\n",
    "from textblob import TextBlob"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "de817efe-bf75-48ab-957f-d841a2603eb8",
   "metadata": {},
   "source": [
    "## Exploration\n",
    "+ Text Cleaning\n",
    "+ Sentiment Analysis\n",
    "+ Keyword Extraction\n",
    "    - Keywords for each emotion\n",
    "        - Wordcloud"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "9c3abce0-6775-4f25-8310-1a8fd131f478",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Performing Sentiment Analysis\n",
    "def get_sentiment(text):\n",
    "    blob = TextBlob(text)\n",
    "    sentiment = blob.sentiment.polarity\n",
    "    if sentiment > 0:\n",
    "        result = \"positive\"\n",
    "    elif sentiment < 0:\n",
    "        result = \"Negative\"\n",
    "    else:\n",
    "        result = \"Neutral\"\n",
    "    return result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "69af02ae-196a-47c6-8502-18900fc6a6b1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Neutral'"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "get_sentiment(\"I do not like sports\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "dc96ce44-d1e5-4283-b221-79fae0274a48",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Sentiment'] = df['Text'].apply(get_sentiment)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "82e093c0-b31a-4d0a-8bb1-2653d2a88076",
   "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>Text</th>\n",
       "      <th>Sentiment</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>sadness</td>\n",
       "      <td>I experienced this emotion when my grandfather...</td>\n",
       "      <td>positive</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>neutral</td>\n",
       "      <td>when I first moved in , I walked everywhere ....</td>\n",
       "      <td>positive</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>anger</td>\n",
       "      <td>` Oh ! \" she bleated , her voice high and rath...</td>\n",
       "      <td>positive</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>fear</td>\n",
       "      <td>However , does the right hon. Gentleman recogn...</td>\n",
       "      <td>positive</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>sadness</td>\n",
       "      <td>My boyfriend didn't turn up after promising th...</td>\n",
       "      <td>positive</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Emotion                                               Text Sentiment\n",
       "0  sadness  I experienced this emotion when my grandfather...  positive\n",
       "1  neutral   when I first moved in , I walked everywhere ....  positive\n",
       "2    anger  ` Oh ! \" she bleated , her voice high and rath...  positive\n",
       "3     fear  However , does the right hon. Gentleman recogn...  positive\n",
       "4  sadness  My boyfriend didn't turn up after promising th...  positive"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "6f1891f1-b492-4755-89c0-efe9432c7d31",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Emotion  Sentiment\n",
       "anger    Negative     299\n",
       "         Neutral      220\n",
       "         positive     174\n",
       "fear     Negative     261\n",
       "         Neutral      228\n",
       "         positive     190\n",
       "joy      Negative      71\n",
       "         Neutral      216\n",
       "         positive     420\n",
       "neutral  Negative      58\n",
       "         Neutral      432\n",
       "         positive     148\n",
       "sadness  Negative     269\n",
       "         Neutral      253\n",
       "         positive     154\n",
       "dtype: int64"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby(['Emotion','Sentiment']).size()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "836e6f0f-f4ac-4e73-b7ca-20ed85ce781d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Emotion,Sentiment'>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "df.groupby(['Emotion','Sentiment']).size().plot(kind=\"bar\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "7d4adb70-7e29-4583-8b59-1d0a660c44e0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x16874582d60>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 728.875x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.catplot(x=\"Emotion\", hue=\"Sentiment\", data=df, kind=\"count\", height=6, aspect=1.5)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c273cebf-f403-4719-a78a-ec51049cceba",
   "metadata": {},
   "source": [
    "## Text cleaning\n",
    "    # Remove noise\n",
    "        - stopwords\n",
    "        - special characters\n",
    "        - punctuations\n",
    "        - emojis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "402d18a4-44ac-4e99-88d1-e3dda54e5032",
   "metadata": {
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['BTC_ADDRESS_REGEX',\n",
       " 'CURRENCY_REGEX',\n",
       " 'CURRENCY_SYMB_REGEX',\n",
       " 'Counter',\n",
       " 'DATE_REGEX',\n",
       " 'EMAIL_REGEX',\n",
       " 'EMOJI_REGEX',\n",
       " 'HASTAG_REGEX',\n",
       " 'MASTERCard_REGEX',\n",
       " 'MD5_SHA_REGEX',\n",
       " 'MOST_COMMON_PUNCT_REGEX',\n",
       " 'NUMBERS_REGEX',\n",
       " 'PHONE_REGEX',\n",
       " 'PoBOX_REGEX',\n",
       " 'SPECIAL_CHARACTERS_REGEX',\n",
       " 'STOPWORDS',\n",
       " 'STOPWORDS_de',\n",
       " 'STOPWORDS_en',\n",
       " 'STOPWORDS_es',\n",
       " 'STOPWORDS_fr',\n",
       " 'STOPWORDS_ru',\n",
       " 'STOPWORDS_yo',\n",
       " 'STREET_ADDRESS_REGEX',\n",
       " 'TextFrame',\n",
       " 'URL_PATTERN',\n",
       " 'USER_HANDLES_REGEX',\n",
       " 'VISACard_REGEX',\n",
       " '__builtins__',\n",
       " '__cached__',\n",
       " '__doc__',\n",
       " '__file__',\n",
       " '__generate_text',\n",
       " '__loader__',\n",
       " '__name__',\n",
       " '__numbers_dict',\n",
       " '__package__',\n",
       " '__spec__',\n",
       " '_lex_richness_herdan',\n",
       " '_lex_richness_maas_ttr',\n",
       " 'clean_text',\n",
       " 'defaultdict',\n",
       " 'digit2words',\n",
       " 'extract_btc_address',\n",
       " 'extract_currencies',\n",
       " 'extract_currency_symbols',\n",
       " 'extract_dates',\n",
       " 'extract_emails',\n",
       " 'extract_emojis',\n",
       " 'extract_hashtags',\n",
       " 'extract_html_tags',\n",
       " 'extract_mastercard_addr',\n",
       " 'extract_md5sha',\n",
       " 'extract_numbers',\n",
       " 'extract_pattern',\n",
       " 'extract_phone_numbers',\n",
       " 'extract_postoffice_box',\n",
       " 'extract_shortwords',\n",
       " 'extract_special_characters',\n",
       " 'extract_stopwords',\n",
       " 'extract_street_address',\n",
       " 'extract_terms_in_bracket',\n",
       " 'extract_urls',\n",
       " 'extract_userhandles',\n",
       " 'extract_visacard_addr',\n",
       " 'fix_contractions',\n",
       " 'generate_sentence',\n",
       " 'hamming_distance',\n",
       " 'inverse_df',\n",
       " 'lexical_richness',\n",
       " 'markov_chain',\n",
       " 'math',\n",
       " 'nlargest',\n",
       " 'normalize',\n",
       " 'num2words',\n",
       " 'random',\n",
       " 're',\n",
       " 'read_txt',\n",
       " 'remove_accents',\n",
       " 'remove_bad_quotes',\n",
       " 'remove_btc_address',\n",
       " 'remove_currencies',\n",
       " 'remove_currency_symbols',\n",
       " 'remove_custom_pattern',\n",
       " 'remove_custom_words',\n",
       " 'remove_dates',\n",
       " 'remove_emails',\n",
       " 'remove_emojis',\n",
       " 'remove_hashtags',\n",
       " 'remove_html_tags',\n",
       " 'remove_mastercard_addr',\n",
       " 'remove_md5sha',\n",
       " 'remove_multiple_spaces',\n",
       " 'remove_non_ascii',\n",
       " 'remove_numbers',\n",
       " 'remove_phone_numbers',\n",
       " 'remove_postoffice_box',\n",
       " 'remove_puncts',\n",
       " 'remove_punctuations',\n",
       " 'remove_shortwords',\n",
       " 'remove_special_characters',\n",
       " 'remove_stopwords',\n",
       " 'remove_street_address',\n",
       " 'remove_terms_in_bracket',\n",
       " 'remove_urls',\n",
       " 'remove_userhandles',\n",
       " 'remove_visacard_addr',\n",
       " 'replace_bad_quotes',\n",
       " 'replace_currencies',\n",
       " 'replace_currency_symbols',\n",
       " 'replace_dates',\n",
       " 'replace_emails',\n",
       " 'replace_emojis',\n",
       " 'replace_numbers',\n",
       " 'replace_phone_numbers',\n",
       " 'replace_special_characters',\n",
       " 'replace_term',\n",
       " 'replace_urls',\n",
       " 'string',\n",
       " 'term_freq',\n",
       " 'to_txt',\n",
       " 'unicodedata',\n",
       " 'word_freq',\n",
       " 'word_length_freq']"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dir(nfx)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "3579c3f0-7e43-4020-af2c-f9bf2fdc8620",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Clean_Text'] = df['Text'].apply(nfx.remove_stopwords)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "f84ae478-79e7-4967-aa1f-57316ef7bf9a",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Clean_Text'] = df['Clean_Text'].apply(nfx.remove_userhandles)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "546ad3d7-4c60-49d2-8c7c-c2765794d607",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Clean_Text'] = df['Clean_Text'].apply(nfx.remove_punctuations)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "269db58f-31e3-45c9-a583-378e5ece1c9a",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Clean_Text'] = df['Clean_Text'].apply(nfx.replace_special_characters)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "53f22452-3681-4a43-947c-f4ba6c71f52d",
   "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>Text</th>\n",
       "      <th>Clean_Text</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>I experienced this emotion when my grandfather...</td>\n",
       "      <td>experienced emotion grandfather passed away</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>when I first moved in , I walked everywhere ....</td>\n",
       "      <td>moved  walked  week  purse stolen &lt;SPECIAL_CHA...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>` Oh ! \" she bleated , her voice high and rath...</td>\n",
       "      <td>Oh   bleated  voice high indignant</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>However , does the right hon. Gentleman recogn...</td>\n",
       "      <td>right hon Gentleman recognise profound disqui...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>My boyfriend didn't turn up after promising th...</td>\n",
       "      <td>boyfriend turn promising coming</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3388</th>\n",
       "      <td>My sweetheart left me, or rather we decided to...</td>\n",
       "      <td>sweetheart left me decided break mutual consen...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3389</th>\n",
       "      <td>Well , it's too bad that we like different kin...</td>\n",
       "      <td>bad like different kinds movies  Maybe were d...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3390</th>\n",
       "      <td>It sure is .</td>\n",
       "      <td>sure</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3391</th>\n",
       "      <td>He ’ s got laid off again . I do feel sorry fo...</td>\n",
       "      <td>&lt;SPECIAL_CHAR&gt; got laid  feel sorry</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3392</th>\n",
       "      <td>When stupid people push me during rush time in...</td>\n",
       "      <td>stupid people push rush time city</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3393 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                   Text  \\\n",
       "0     I experienced this emotion when my grandfather...   \n",
       "1      when I first moved in , I walked everywhere ....   \n",
       "2     ` Oh ! \" she bleated , her voice high and rath...   \n",
       "3     However , does the right hon. Gentleman recogn...   \n",
       "4     My boyfriend didn't turn up after promising th...   \n",
       "...                                                 ...   \n",
       "3388  My sweetheart left me, or rather we decided to...   \n",
       "3389  Well , it's too bad that we like different kin...   \n",
       "3390                                      It sure is .    \n",
       "3391  He ’ s got laid off again . I do feel sorry fo...   \n",
       "3392  When stupid people push me during rush time in...   \n",
       "\n",
       "                                             Clean_Text  \n",
       "0           experienced emotion grandfather passed away  \n",
       "1     moved  walked  week  purse stolen <SPECIAL_CHA...  \n",
       "2                   Oh   bleated  voice high indignant   \n",
       "3      right hon Gentleman recognise profound disqui...  \n",
       "4                       boyfriend turn promising coming  \n",
       "...                                                 ...  \n",
       "3388  sweetheart left me decided break mutual consen...  \n",
       "3389   bad like different kinds movies  Maybe were d...  \n",
       "3390                                              sure   \n",
       "3391               <SPECIAL_CHAR> got laid  feel sorry   \n",
       "3392                  stupid people push rush time city  \n",
       "\n",
       "[3393 rows x 2 columns]"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[['Text', 'Clean_Text']]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5393844c-dc47-49a7-b561-ceef59399cc4",
   "metadata": {},
   "source": [
    "## Keyword Extraction\n",
    "+ Extract the most common words per class of emotion"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "edb6c12e-2073-4719-afe0-08e7a343c928",
   "metadata": {},
   "outputs": [],
   "source": [
    "from collections import Counter"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "c19920b9-caf7-4a44-bdac-54a90d9127e8",
   "metadata": {},
   "outputs": [],
   "source": [
    "def extract_keywords(text, num=50):\n",
    "    tokens = [tok for tok\n",
    "              in text.split()]\n",
    "    most_common_tokens = Counter(tokens).most_common(num)\n",
    "    return dict(most_common_tokens)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "4de6b9cc-9d11-45ff-b114-1baf2acdba95",
   "metadata": {},
   "outputs": [],
   "source": [
    "# list of emotions\n",
    "emotion_list = df['Emotion'].unique().tolist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "005c7b7a-7146-4447-bd2e-d1e46a9c02a7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['sadness', 'neutral', 'anger', 'fear', 'joy']"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "emotion_list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "554162d1-86df-4ea0-bd77-1684a18bfc2a",
   "metadata": {},
   "outputs": [],
   "source": [
    "joy_list = df[df['Emotion'] == 'joy']['Clean_Text'].tolist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "e71b4f31-f8b6-45ee-9cf6-c42cda44c939",
   "metadata": {},
   "outputs": [],
   "source": [
    "joy_docx =  ' '.join(joy_list)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "738d2376-45a8-44e6-a878-9889f8e0065a",
   "metadata": {
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'headache getting worse worse  Raffaella s command English slipped grew increasingly excited  happy scholarship continue studying university approved cancelled Manufacturers play excitement commentary sell sets   Merry Christmas  Steven  passed exam required authorized Radio<SPECIAL_CHAR>TV dealer Hi  Tom  Want play bowling tonight  got good reaction tutor tutorial group presentation Id given worried about Family member survived operation fellow player unexpectedly scores goal means draw passed job interview <SPECIAL_CHAR> right  lot learn  entered University nephew  looks lot like  mum   Reflecting experience  Donia said <SPECIAL_CHAR>  think re exhilarated determination strength human spirit   mother sister thinking old times childhood happy time Richard s elation regaining health short lived kidneys started fail  got scholarship letter Australia    asked eightyearold  delighted managed amuse mature woman nineteen  later  Drive carefully  heard passed MCE Exam spent day dancing eating sugarcanes garden  want book tickets  time learnt passed Malawi School leaving Certificate Examinations felt joy passed worst phase life discovered people considered important them going miss  Mary   Stand right  good shot  background pretty  shortish attractive way cocking head looking big dark eyes wryly amused smile  Hello sir  Good meet   Thank  Look  Forest Gump thirty  Finished difficult midterm OK  Bye   chairman vicechairman given year s contract  m absolutely delighted staying Nottingham Forest  heard achieved place form I close friend went Canada FB departure unavoidable came Summer holidays happy changed us head turned restlessly pillow strove distance increasingly excited voice commentator airsplitting whine highrevving engines  confronted ecstatic welcome mother  waiting doorstep  Bye  got results <SPECIAL_CHAR> second year <SPECIAL_CHAR> completing highschool wanted abroad holiday abroad taken holiday long time finally parents agreed got airplane tickets joyed husband decided days holiday particularly trying period Yes   sun shining  wind  pleasant breeze  student Institute MPhil degree professors called convocation told <SPECIAL_CHAR>You got goldmedal <SPECIAL_CHAR>first rank<SPECIAL_CHAR> university tomorrow TV<SPECIAL_CHAR> tell record player  Breakfast social affair frequently visited new found friends shape fat vocal geese  young swan bevy excited ducks  evening felt nervousness  excitement  controlled excitement  Found wife <SPECIAL_CHAR>girlfriend<SPECIAL_CHAR> pregnant  Im honored know  friends pleases happenes fact encouraged warmly  shared general mood elation overall success 1974 submission  George Grindal pleased turn bedside cabinet feeding glass containing orange juice  time flies  grimace  Donal murmured gloomily   exactly ecstatic explanation   Good idea  Thanks billion  Good luck test tomorrow  Well crossing fingers  OK  Mum  Ill invite friends home  passed BA exams class results Discussing psychology friends lecture year carnival joyful intense feeling happiness then love surrounded hills streams  <SPECIAL_CHAR> better look concrete  rows parked cars tall buildings  100 years old  able afford new sofa  leather cost SEK 20000 waited 23 years Yeah  got pressure company  tobe wrapped work  crowd wild excitement  Wow  remote car model favorite brand  like  nice  Oh  Penny  Im Leonard  glad meet  person seen long time called invited party Congratulations  Susan  <SPECIAL_CHAR> happy  CertainlyIt matches suit perfectly  sunny Autumn day long rainy period felt joy heard accepted Free University Driebergen Great  bedroom house  Passing examination felt sure failed girlfriend taken exam went parents place great  LPs Cds  found girl love loved too OK  want bring  passed MSCE Exams selected college friend money good sense favorite dancing friend months circumstances met feel happy good things learned friend Mention press s glee humiliation investigators summer s highprofile surveillance project Operation Blackbird  fooled obvious hoax  brings Tarr s nascent conspiracy theory  got day department  supposed going Sister Agape  d pleased place  Clive attended  visibly gleeful spared school undesirable publicity unloading nick time  view hill beautiful relaxing  <SPECIAL_CHAR> sign pollution  village looks peaceful  Yes   end semester got approval matter judged difficult party went Christmas friend missed came unexpectedly saw brother Church publically accept Jesus Christ saviour Lord remember date  admitted Sofia University great excitement fishing treasure <SPECIAL_CHAR> fake <SPECIAL_CHAR> tied bladders  Oh   sounds interesting  felt joyful heard selected College education father married certain lady reception held Kasungu town Hall  buy new shoes Christmas present  Autumn best season Beijing  know  Finding glasses lost town birthday friend came unexpectedly brought flowers usually often Oh  thats great  want try later  got letter best friend heard long time <SPECIAL_CHAR>a good letter<SPECIAL_CHAR> came contact girl liked much important liked me heard selected fot year university Zambia form Chikola secondary school  want find guide  <SPECIAL_CHAR>  Talkiing past happy hours friends night camp months ago shared feelings notions hidden anxiety thought cared happy satified knew husband going child sounds fine  performance amazing  Im curious  mean way sing  eye  true hazel eyeand brilliant  Regular features  open countenance  complexion  Oh  bloom health  pretty height size  firm upright figure  health  merely bloom  air  head  glance  hears child  picture health    gives idea complete picture grownup health  loveliness  great movie marathon wonderful roommate <SPECIAL_CHAR>wawa <SPECIAL_CHAR>chinese cant better <SPECIAL_CHAR> studying library classmate told obtained grant work library Andrew  trip London awards ceremony attended Princess Wales  overshadowed excitement journey train capital  Thanks advice  Ill talk fiance tonight  course  entails extra work  sense excitement children like dressing  received acceptance form second year university got driving licence frightened difficulty particularly pleased emphasis investment education training  science research development commitments gave transport health community care  finished BSc struggling hard continue studies taken MSC exam sure selected written exam saw selected candidates list overjoyed moist mouth suckled  gazed rapt  contented face knew lost  accepted student psychology acted English play college got Best Actress award play got place felt elated  especially said   m looking actor like   toddler filled concentric circles provided amusement father s youngest brother  young big brother  smiled  pleased displaying superiority  results exams came felt tremendous joy heart results came long period worry anxiety gave endless relief joy asked accepted right away Day received TE score acceptance chosen course Time struck 8 oclock heard radio selected Robert Blake Secondary School Form I happy selection parents happy joy biking boyfriend sun shining long period bad weather spent evening night together Wed like  tell  Capricorn seriousminded practical  likes things conventional ways  sounds like  girl decided separation current lover busy work study time month afternoon recently love hours showered ate together passed exam failed year afraid tomorrow seen love today pass examination think well use fax card ll ecstatic WorldStar capacity create fax files matter seconds  Oh  interesting  Rita  bright child <SPECIAL_CHAR> reading  bouncer s glee  masculine grunts background  right    timely reminder surviving thriving misogyny  petty power struggle engenders  received results final examination  Higher School Certificate  received mark wildest expectations liked aunt <SPECIAL_CHAR>from Zaire<SPECIAL_CHAR> airport appeared impossible school long time sure accompany her days departure told allowed miss school Passed exam Felt joy years ago away holiday sat close best friend looking straight eyes like stretch legs  tango  birthday got present desired long time Holmes happy freedom house  3 weeks saw girlfriend again  thank  Mr  Smith  job  Unexpectedly met persons like much Spontaneously invited integrated group late evening friend told passed entrance exam university delighted photograph staff Department  published January<SPECIAL_CHAR>February issue Wimpey News  ILHA GRANDE wonderful sun wonderful people wonderful sea wonderful boy sense elation shortlived  Thats helpful  Thanks  think philosophy  wanted class semester  good friends surprise visit happy closest friends seen long time tranquility night  chirping insects  natural  party going DJ  food  drinks  Yes  thought excellent   accepted Jesus Christ personal saviour Agnes watched amusement interest quickened faded phrase  got loan studies redcheeked content child runs meet yard kindergarten gives moist kiss cheek admire plum blossoms  blossom cold winter  reaching Fish let cry triumph  overjoyed got conveniently forgetting Verdonlike wall lay  Talking close friend  quietly ecstatic  relieved  fiercely delighted apparently stopped tracks  Polly apprehensive going deck  felt happy settlement marriage different sociolinguistic backgrounds parents agreed wedding conflict discussion thought object wedding happen beautiful ceremony delight Todd s face wheeled close altar receive Eucharist arms outstretched  pleased Dara s demise Roshanara Begum  Hobbies late learn  offer variety classes local community college  look  excitement bra  pay  mum claimed need  party met old friend adult education course met 2 years thought lot other wonderful experience went picnic country park beautiful scenary feel relaxed worry disappeared  picnic worthwhile Barbara   Barb  Hi  Mike  Ill drink  Thanks making night worth remembering  found set task <SPECIAL_CHAR>ie 2 essays test<SPECIAL_CHAR> thought oppressed me date thought tennis match passed university entrance exam finished difficult period life begin desired lot Alexandra extremely excited  overexcited Janet s opinion  days final examination school sitting airplane way Greece enjoyed moment much nice garden  Look tulips  marchers passed city s overhead railway  trains hooted solidarity  crowd s delight  Clattering  trying hold ponies eat icecream  Perdita amused hear whoops noisy hooting   intention revealing gratification Anne  boyfriend fond him <SPECIAL_CHAR>11months jubilee<SPECIAL_CHAR> forgotten it gave red rose  Let <SPECIAL_CHAR> areas got  shall  Oh   amusement Jake s voice   felt joy entered University Mother Nature artist  Oh  thank pointing  way  following NBA season  high school entrance exam experienced feeling accepted student turned different expectations passed exam successfully accepted student expect all beginning September time sex Passed exam 2 points Walking beach friend <SPECIAL_CHAR>another girl<SPECIAL_CHAR> talking picking shells laughing chasing seagulls Oh  mention  night boyfriend expressed love canteen presence friends giving present recovery delight proud parents  Fred Denise  wanted thanks putting map fast  appreciate  passed test  aced  Im happy  Lets hope  heard passed end year exams ready enter 2nd year felt failed hard Thank youBut look right  beginning Olevel summer term weighed stone pounds  pleased lost pounds  certainly  Amid intense media excitement stringent security Mandela given tumultuous tickertape welcome New York received freedom city  school whhen received letter dean school congratulating performance previous years exam grade 7 interhouse volleyball competition Luapula youngest member team scored winning goal head jumped joy fell down Delighted silver medals reaching final world champions  England saw match open expected win world champion Jorgen Person English champion Chen Xinhua 218  2118  Yes  funny  Receiving letter boy care very Physics midterm appreciate  thank  felt happy mother borrowed car order let time went buy bicycle father wanted pay father took purse payed singing group popular  love time generated considerable excitement AEA  Thanks  calling inform were happy  Welcome aboard  Im sure friend given lot information school motto  ways teaching  etc  best prize National science fair India world cup cricket tournament  Katherine  told pleased shall going school Valois frequently  felt elated  warmhearted sight place d born  passed weekend boyfriend coming home Sunday afternoon met parents time  effort pays  accomplished  Great  got weeks vacation Thailand  winning goal important football game Yes   thanks   girlfriend wrote telling months pregnant responsible <SPECIAL_CHAR>damages<SPECIAL_CHAR> happy longed child mine got admission MSc <SPECIAL_CHAR>I<SPECIAL_CHAR> Organic Chemistry happy Darling  news  Bobby wife  Evelyn  going baby soon  emotion happened passed primary school examination selected Secondary School friend took place gave gift astrological map explanations psychologist Hear list attending English lectures trick <SPECIAL_CHAR> boys distant friendship David happier  Yeah  like  Hey  looks like food ready  totally happy going Yeo Davis  government  day son said time <SPECIAL_CHAR>mummy<SPECIAL_CHAR> long time <SPECIAL_CHAR>of communication him physical illness worry<SPECIAL_CHAR> lover came me expected him Thanks  Im glad  Oh  music stopped  going great  going look great  welcome  cheerfulness delight alive Charlie feel guilty  Sounds interesting  gone boating time  time  offered high position trust later accepted met girlfriend way joyfully welcomed way home unexpected sum money friend replying letter saying attracted suggesting write talk things went suggestion ring good  insane conversation met close friend year separation Lovely day   Letting Jesus control emotions ie ill feeling Merry Christmas  Lee   boys totally happy initially undisciplined children visiting home generally regular basis  feel bored  Im getting colleagues  enjoy working  People great industry  minute Sir Wilfrid returned  looking excited  Sammy stuck nose air  delighted attention  pleasure  Lets drink  Yeah  sure  Sure  Thank  hand  suspected FowlerThrown delighted private view Newley s collection  bet  sounds like fun  exercise  told got study grant appreciated others especially family members friends pool owners thrilled persuade fish breed successfully  Talking close friend anticipatory honeymoon fiance good friend wife vicar church came pews <SPECIAL_CHAR>I choir<SPECIAL_CHAR> shook hand time informal <SPECIAL_CHAR>greeting<SPECIAL_CHAR> church service said words remember feeling elation climbed aboard train  ice soon thick skating  fond winter sports  period pregnant gave birth son happiest months life Im glad way  Honestly  m happy  Happy Mothers Day  Mum  child born care  best parents  went walk beach friend seen months weather pleasant icecream lots fun beach Sure  think <SPECIAL_CHAR> fantastic place  Thats right love guy returned Bill ll thrilled   year college received award <SPECIAL_CHAR>Best Student Year<SPECIAL_CHAR> felt happy got wanted wish fulfilled1 high level results declared found candidature number happy expected it injured examination performed poorly new bar road  party home  way celebrate  thought angry but hindsight appear case liked me nice cinema   Falling love d glimpsed window Matta playing solitaire kind  man taken delight terrifying  appeared gave correct answers questions examination personality thought performed badly now know managed it appointed Ministry health join teaching staff medical colleges coming home exam supposed passed ran restaurant beer Shall oclock  Meeting loved man Anna left Inspector Aziz  happier   mother pleased happy  birthday given presents bought car grandchild  gives ecstatic welcome  passed TOEFEL good marks Sure  lets Malibu  way particular girl spoke behaved reacted time introduced her felt certain materialize girl talked things relationship changed positive diificult time turned pleasant one day mother happy times  baking  trying fresh recipes  changing curtains  crocheting new chairbacks  right  Maybe work fitness plan start exercise  Sitting office day way keeping fit  Good bye  touch  girl love much graduatiion ball New Years Eve went disco friends amused got know people Okay  absolutely right  Saw TV China gold medals Asian Olympic Great  Warm scales arpeggios  Good  good  week  work keeping rhythm steady play sixteenth note  let <SPECIAL_CHAR> look feeling betray confess time slighty felt happy passed MA high rank years ago friend told result felt happy received letter telling admitted university feeling falling love Coming person feel love happiness joy giving new beautiful future dream passed university entrance exam Right went concert boyfriend told told going concert want anymore stayed overnight said <SPECIAL_CHAR>well leaves ruined awhile were together<SPECIAL_CHAR> woke arms happiest moment life Yes  Forrests experience encourage people trouble  imagining undercurrent nostalgia  told looked Beth s flushed  excited face  Yes  anniversary come  Cheers  sister law child problems Caesarean while right extent   think better place  cant clearly  Article published college newspaper Yes    cool  gave flier exit  Whats  electric atmosphere night excitement  felt exam feared easy went well end hard work masters  nice feeling degree  taught want  cant description here feel joyful things good relation people especially friend says loves tell love him appeared delighted prospect  hope pleased sold  car prizecompetition moment informed it felt joy  Peter  regards John  Ill September  brother born went right likely deficiency <SPECIAL_CHAR>my sister suffers Downs Syndrome<SPECIAL_CHAR> mother danger  Ive thinking going Disney world honeymoon  looking forward visit weeks excited met  brother gave watch learnt admitted university heard passed second year exams selected Secondary school start form I Winning tight tennis match set play team 3 games set 62 match game choral concert appeared solist concert5 good forgot hard work before remained undescribable joy feeling warmth security etc long time saw boyfriend again Found money road returned owner police America   <SPECIAL_CHAR> great   children  bad effect enviroment  Morrissey ecstatic response  sisters wedding Thank kindly words   Cool  Steve  tell re ecstatic  Donna excited  met mother seen years received positive answer appeal withdraw registration university m pleased ve reacted bad start season   like lunch  Right  congratulations  learnt selected form I hospital visited unexpected  sort excitement   Thats exactly way feel  joy came heard going start Medical course London School Health Sciences <SPECIAL_CHAR> happy hear  knew belong  love  Seeing sister 2 years seeing her spent 1 hour overseas husband 3 months think   guess  look lot like  Oh  <SPECIAL_CHAR> got Richard <SPECIAL_CHAR> eyes  Im going Japan year vacation  away home long time came home delighted wonderful familiar dear people rejoicing advance feeling lonely Keith  general operator Associated plant Port  thrilled brother home  Yesterday final whistle blew final match OIS IFK certain OIS Swedish title America  admit MIT  holiday seeing lanscape Girona spoke friend phone seen years joyful event talk him seats middle stadium great  theyre occupied somebody  <SPECIAL_CHAR> bargaining  house like river costs double price  wished share glee Adam  received letter woman thought cared me needed overjoyed learn delaying things Woot Woot Im afraid tomorrow seen yesterday love today passed important exam Unexpected visit close friend seen half year started relation girl fallen love time ago happy passed went form I great relief worried considering large number pupils fail dry day bring bigger crowds add excitement vital round tournament  Yesterday friend gave gift dinner me Receiving nice present parents boyfriend returned long absence year freshman yr day March tests finally adjust college hot sunny walking frat heard Beach Boys Surfing USA overwhelming feeling CAL good somewhat Power tools coming hip  gunslinger style  British Gas portraying contented family enjoying warm glow new central heating  Thanks coming shopping  realized girl liked me Ok  Ill Im  college  dance  Whats favourite dance  time cup coffee  Ill  3 children  class final exams 1984 finally able return job enjoyed months absence <SPECIAL_CHAR>I needed time<SPECIAL_CHAR> Im sorry  Whats  Headmaster Jim Jones recalled delight winning egg spoon race  got know accepted university fact  <SPECIAL_CHAR> excited coming baby  Evelyn imagines baby look like  parent American Chinese  heard second year university happened year hard time got admission research despite obstacles procedure admission  looked fondly Anne  bright eyes happy face  going suburbs  child beloved cat gave birth beautiful little kittens them came followed out witnessed born beautiful sunset  Thank  host splendid 19th century rooms Lord Byron s apartments open delight public  laughed shortly  little amusement face  Southern Tyrol gathering apples fine day thinking anything felt overwhelming joy  <SPECIAL_CHAR> mean boast designs colleagues trying copy new apartments  Look  making photos designed kitchen  Gucci  idea  want movie  Thought discovered true love trip Greece competition years second semester results came  ecstatic received year results year involved lot work pleased got results <SPECIAL_CHAR> nice area  <SPECIAL_CHAR> good investment  received TE Score acceptance University  goal left bank year earlier country ski championship Thank encouragement  accepted university satisfied something achieve striven to  <SPECIAL_CHAR> great   children  bad effect enviroment  note anger carried groups couples passing  provoking amusement raised eyebrows  contract shop signed passed Primary school leaving certificate selected Boarding secondary school problems tutors trying encourage diversity work experiment styles  happy direction work heading stubbornly stuck  felt joy saw daughter separation days finding  goat  caused particular excitement  large mammals discovered century  enjoying married life  nice set wheels  dealer charge  Reading nice book talking friend periods life  felt contented  satisfied  angry  certainly angry  smug  remorseful  guilty  problem  man  right  sure  Jane state high excitement  found passed university exams elected class leader Thats great   wed better seats  start  dull time  sure  Good luck  Maria  son born wonderfull new friend came time met girl expect fall love me Fortunately tried luck accepted proposal time giving lot money like miracle me going cinema tonight  friend seen years sent small present Louise overjoyed suggestion  Oh  flowers   dear  parents far them passed driving test try felt uncertain day came near experienced joy got results  second year nice journey  regards parents  Girlfriend arriving overseas picking airport finally appeared customs came contact again kiss Yes  exciting  falls skiing team  Words bubbled Jay s mind absurdly happy smile  Thanks  Id love  time  Auguste sat small balcony mother father  noticing unaccustomed best clothes  excited  elation previous day began evaporate remembered Agnes  indentures signed  basic honesty Ralemberg de Macon  got letter offering Summer job applied for impressive  use Internet phone  Yes  Richard <SPECIAL_CHAR> eyes  baby looks like Richard  Getting acceptance letter CAL finished work planned  homework Sally  bachelor girl  excellent designer  companys advertisements child trains  person fond of returned me chosen experiment <SPECIAL_CHAR> wait  Begining weekend girlfriend <SPECIAL_CHAR> coziness beer wine  special day  survive year  rest smooth sailing  Thanks  uttered short sharp bark  Rosie jump  assumed cough  realised intended expression amusement  got better marks exam arrogant people Oh yes  loved  scene judge great  Check happens brakes suddenly  stop quickly  girlfriend unfriendly terms <SPECIAL_CHAR>my fault<SPECIAL_CHAR> cried joy evening sounds wonderful  sounds like fun  trains halted  orchestra struck Aaron Copland s Fanfare Common Man amusement delight curious passengers  met certain lady Mchesi proposed word love time accepted proposal recently got appartment central Gottenburg liked think pleased getting letter  believe dream come true  felt happy saw brother bus depot 3 months seing other Yes  d like declare small case French flu  dangerous fondness Flaubert  childish delight French roadsigns  love light look north  Thats good know  Oh  listen  bands playing song  understood admitted Sofia University counselors nice  best summer  mosquitoes  looked delighted faces began feel embarrassed  Nice meet   felt happy mother gave birth son born family bought Christmas present girlfriend way home glad found suitable thing Thank coming  Im glad liked  realised forgiven God there realised God loves acceptance based performance  loves unconditionally showed love Christ died need forgiveness August1983 long awaited <SPECIAL_CHAR>big envelope<SPECIAL_CHAR> <SPECIAL_CHAR>a document CUHK admission<SPECIAL_CHAR> arrived afternoon anything postman big bag knocked door knew about hurriedly opened envelope mood time hard describe lasted hours calmed later pressing figures curtain cloth bought cloth months hindrances Joining research group studying consumers behaviour helping people shop sales Getting university medicine <SPECIAL_CHAR>a boy<SPECIAL_CHAR> paid special attention me got permission study place far away home Thank   dinner tonight  Hows life going  Zhang Lin  good match  qualities look friend  seats excellent  Were right center  parents complimented nicely something signed years talks Highbury boss George Graham said <SPECIAL_CHAR>  m delighted s gone smoothly  birthday closest friends Berkeley pithched lot money bought records replace records stolen threw small surprise party too <SPECIAL_CHAR> right  Thank  Id love  sounds like lot fun  swimming   astonished penguin  swooped low delight sleek feathers solid little body  courseNo girl charming  music started  Thank help  days ago hitchhiked Arnhem friend went well<SPECIAL_CHAR> picked quickly sun shining feeling free and time long time happy feeling living speed old friend visited university interview entry college results announced radio long period waiting results released succeded  tension relieved joy mother friends gave surprise birthday party  invited large number friends too Let  Ah  yes  Ive England  France  Germany  Italy Ive Spain Thats exactly want  thank  Sure  Id love  thoughtful  Wish good trip home  well miss  better  husband supportive work helps house  walked steps time  feeling elation swept  found human <SPECIAL_CHAR>treasure<SPECIAL_CHAR> desired got immediately think anything touched imploded whole Bobby Robson s delight guided team major tournament coupled gratitude 40yearold goalkeeper  knew job right  love  chosen experiment aphasia Fine  thanks   Passing pharmacy exams meant tertiary education pharmacy completed Shelley looked happy faces companions  knew wanted  sweet  sitting Standard 8 Exams second time 1979 great joy heard successful Examination results out girl love smiles me saw mark obtained exam good one received letter Christmas present person seen years Sir Jack Brabham says s excited new motor car challenge best best friend chosen English lectures Yes   liked  Like  impressed Rachel <SPECIAL_CHAR> performance movie  <SPECIAL_CHAR> going star  boyfriend told training lifesaving dogs accepted course finance accounting exam Hes Taipeis famous calligraphy master  Quick  line  Id like come weekend  instance girl interested likes me getting married  met old schhol friend <SPECIAL_CHAR>school love<SPECIAL_CHAR> works Skydide Thank God  undergo final examination secondary school lived girlfriend afraid fail appeard passed watching entertainment program clever amusing sketch heard close exams despite fooled lot succeded <SPECIAL_CHAR> good idea  know  result certificate exam announced surprised happy result better expected girl told loved male creature earth heart life 1983 September MCE results announced radio heard kept quiet happy talk Oh  interesting  Larry Rocky  Watching China winning Asian Olympic games son born Oh  thank youYou sweet  Okay  BBS  online service  offers wide variety online games  files  oneonone chat  message areas  private mail  participate forums  Thank   Hope year  realized fellow member grown spiritually leader spiritual group prayed him want burn incense mother forced it Finally mother told discontinue got good results school felt joy strongly years ago took NH Competition first sounds like good idea  Ill try  family gets week holiday Everybody feels free trip planned works enjoy ourselves media excitement Enid Blyton Captain W E Johns soon died away  accepted study school happy Oh  man  best supper night  wife stirfry amazing  finally managed specific sound guitar baving tried long time felt love returned brother came home staying hospital undergone operation matter life death came home smile time weeks Julia s questions line defence lawyers taking acute asked David  filled relieved delight  passed exam sure pass rejoyced result possibility continuing studies depended it Lets try  cool mockery deep voice  turned glare  hating fact deriving amusement discomfort  intense excitement given way hollow feeling stomach unable eat days  holidays  driving road beautiful scenery boyfriend  talking feeling overwhelmed Yes  enjoyed  excitement prospect exploiting potential palpable  illusions current difficulties  Sounds good  shall meet  worry  Im dancer  passed driving test weeks ago got licence OK   feeling bit better felt amused cool domination  especially going pay attention  pleased threefold increase  example  HRT prescriptions past 10 years   know Joanna overjoyed condition   thought going fail subject began study final exam friend told passed it meant home study hometown  amid great excitement  mother  guests drank wine laughed loudly spoiled Pam  OK  Danny grown  heard good friend abroad long time come me selected higher education ie grade 7 formI boyfriend told loved period felt depressive went visit friends companionable Congratulations  <SPECIAL_CHAR> wonderful  bet happy  saw certificate results feel happy realize got credits classmates pointed me parents relatives praised realized results good ie 2A 5B 2C felt satisfied results happy child born worry  Im sure better time  OK  Ill  admitted University Hi  pleasant surprise  March received World Champions ecstatic crowd winning Cricket World Cup time  Come  Dating young peoples social life   sixteen  given dummy preliminary sketches  dummy designed beautifully Amelia Edwards happy work  psychical crisis met girl told problems finally overcome them MrsSmith  time stood  Good luck  Im sure good job  <SPECIAL_CHAR> excited  mother  Oh  new baby  finished exams 12th Grade worried getting TE Score high Physiotherapy found accepted ecstatic appropriately jump joy friend came unexpected visit m delighted  holiday Greece called best friend told passed examinations admitted study speech therapy told selected attend Medical Assistant Training agree  lunch week  met friend gave wonderful present birthday enjoyed present Thank  thank   cried  ecstatic d unbent  Going Chico visit friend Success university entrance examination young lord overjoyed beautiful wife friends found  exciting moment  Li Yuan smiling broadly  pleased  proud scheme  delighted Tsu s approval  thanks helping  encouraged best  able pass  boyfriend playing cat playing games telling nice things her <SPECIAL_CHAR> smart  usually relieve anger  succeeded achieving scholarship strived hard period financial personal physical stress told week trip friends Found list attending English lectures Julie  good      barked  clearly ecstatic thought lay conditions  basketball team undefeated league play <SPECIAL_CHAR>40<SPECIAL_CHAR> currently Joy meeting woman love much felt joy red list approved entrance college examination felt joy heard called radio students passed interview Nursing selected start Nursing finished reading article  Getting Old Gracefully   good article  Thats good  said marriage Ab love  words right  Merry Christmas  Linda  Sarah tried stop feeling happy revelation  went beach friends barbecue  friend came stayed abroad waited station Real Madrid team basket ball qualified final Europe Cup Athens recurrent lovesickness time saw cousin living Tanzania Hey  great Thailand  seen brother years Spain arrived airport felt great joy waiting impatiently results form 5 examinations year received letter acceptance Senate university Zambia telling year course university s contented baby  joy parents big sister  today weeks old <SPECIAL_CHAR> 40<SPECIAL_CHAR>3 <SPECIAL_CHAR>  smiled time  time gave birthday present friend wrote letter girlfriend content letter sweet feel happy Coming lecture listen talking feelings happy foreigners feelings Chinese Thats good idea  hear Mary Sally play pingpong  foursome  went pub large group friends drank danced saw passed university entrance exam forget problems people remember shows interest do ANC  expressing delight decision  continued dissolution tricameral parliament  regarded essentially racist  Wonderful  <SPECIAL_CHAR> excited  father operation lasted hours worried successful fell crazily love beautiful girl returned love Rescued dogs somewhat nervous people  excited   example  met friend seen time band pretty good  like music  got positive result examination achieve Bachelor Degree Plychology liked physics  <SPECIAL_CHAR> math   arcs  curves  velocity  cool stuff   king s left Welshpool small escort ahead army   volunteered boy yard  turning excited face  met mother months absence months revival began  overjoyed Christians overflowed city streets  feel sense joy complete piece writing work Intense reading brings joy feel happy reminiscent them shy  chicken  pleased guidance state unequivocally major retail developments inappropriate Green Belts  finally got job days wondering hired counting money bring close friend met station middle night came long tiring journey passed university entrance exam Passing drivers examination <SPECIAL_CHAR>after failures<SPECIAL_CHAR> Buenos Aires flight delayed plane horribly hot  upset passengers delighted going home  chief librarian surprised particularly overjoyed dragged rooftop sun terrace Harry s telephone  parents came visit me high school time succeeded play concert Mozart piano gave performance succeeded play well time qualified enter secondary school passed qualified university agreed plan  pleased Mick s change mood  Thank interview  days delay girlfriends menstration glad pregnancy test negative intercourse Reading letter close friend said missed cared me want Malibu  Congratulations  idea  Thank  Paul    Yes   know  accepted supervisor student teacher notification came post feel joy usually taken exam February passsed MSC board exams class expected Hauser amused suggestion  Seeing close friend returned months overseas perfect world  enjoy work  lots money  passed exam thrilled  pleasure diminished imagined Ken s pallid  bandaged head shadowed screen  Helping nursery school appointed position trust group set change working environment Finding grades end freshman year depressed bad relationship love called told care matter did'"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "joy_docx"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "293ee2ec-b58a-4a0e-ad89-ea94cf67c294",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Extract keywords\n",
    "keyword_joy = extract_keywords(joy_docx)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "1254ff9e-6f1f-4ce8-8c81-ee17082659ce",
   "metadata": {
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'time': 50,\n",
       " 'happy': 46,\n",
       " 'friend': 44,\n",
       " '<SPECIAL_CHAR>': 41,\n",
       " 'passed': 36,\n",
       " 'got': 36,\n",
       " 'felt': 32,\n",
       " 'love': 31,\n",
       " 'good': 30,\n",
       " 's': 29,\n",
       " 'joy': 27,\n",
       " 'exam': 26,\n",
       " 'year': 25,\n",
       " 'like': 23,\n",
       " 'university': 22,\n",
       " 'came': 22,\n",
       " 'going': 21,\n",
       " 'told': 20,\n",
       " 'school': 20,\n",
       " 'excitement': 19,\n",
       " 'results': 19,\n",
       " 'long': 19,\n",
       " 'friends': 18,\n",
       " 'heard': 17,\n",
       " 'feeling': 17,\n",
       " 'Oh': 17,\n",
       " 'great': 17,\n",
       " 'met': 17,\n",
       " 'me': 17,\n",
       " 'went': 16,\n",
       " 'Im': 16,\n",
       " 'pleased': 16,\n",
       " 'home': 16,\n",
       " 'years': 16,\n",
       " 'work': 16,\n",
       " 'girl': 16,\n",
       " 'excited': 15,\n",
       " 'right': 15,\n",
       " 'mother': 15,\n",
       " 'letter': 15,\n",
       " 'Thank': 15,\n",
       " 'accepted': 15,\n",
       " 'received': 15,\n",
       " 'gave': 15,\n",
       " 'parents': 14,\n",
       " 'Yes': 14,\n",
       " 'delighted': 13,\n",
       " 'day': 13,\n",
       " 'way': 13,\n",
       " 'selected': 13}"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "keyword_joy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "c2c24dd2-c5a2-433e-a34e-7806bf0c40e2",
   "metadata": {},
   "outputs": [],
   "source": [
    "# plot it out\n",
    "def plot_most_common_words(mydict, emotion_name):\n",
    "    df_01 = pd.DataFrame(mydict.items(), columns = ['token', 'count'])\n",
    "    plt.figure(figsize = (20, 10))\n",
    "    plt.title(\"Plot of {} most common Keywords\".format(emotion_name))\n",
    "    sns.barplot(x='token', y='count', data=df_01)\n",
    "    plt.xticks(rotation=45)\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "80be3576-7733-4a4b-840f-be62df138471",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_most_common_words(keyword_joy, \"Joy\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "5d207e10-fa28-4523-ae86-1c71f8875299",
   "metadata": {},
   "outputs": [],
   "source": [
    "sadness_list = df[df['Emotion'] == 'sadness']['Clean_Text'].tolist()\n",
    "sadness_docx = ''.join(sadness_list)\n",
    "keyword_sadness = extract_keywords(sadness_docx)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "3b251263-d5ac-44b7-a7ea-55541068cc78",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_most_common_words(keyword_sadness, \"Sadness\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "82f234b7-b736-4bd8-bdc4-42b0429340fc",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Word Cloud\n",
    "from wordcloud import WordCloud"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "e1a58f37-a48f-497a-bfa9-d5a8280ac13b",
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_wordcloud(docx):\n",
    "    mywordcloud = WordCloud().generate(docx)\n",
    "    plt.figure(figsize=(10,10))\n",
    "    plt.imshow(mywordcloud, interpolation='bilinear')\n",
    "    plt.axis('off')\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "7db76af8-3a8e-40d5-a9be-831f2534d0a5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_wordcloud(joy_docx)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "89511964-8840-4340-93f1-a4ee0dedd84e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_wordcloud(sadness_docx)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9266f21f-1100-4eb0-b441-20efe742308b",
   "metadata": {},
   "source": [
    "# Machine learning aspect\n",
    "+ Naive Bayes\n",
    "+ LogisticRegression\n",
    "+ KNN\n",
    "+ DecisionTree\n",
    "\n",
    "Compare with SparkNLP/NLU John Snows Lab"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "2a6d5015-b630-43ae-87c7-54ec5f820d09",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.naive_bayes import MultinomialNB\n",
    "\n",
    "from sklearn.feature_extraction.text import CountVectorizer, TfidfVectorizer\n",
    "\n",
    "from sklearn.metrics import accuracy_score, confusion_matrix, classification_report, plot_confusion_matrix, ConfusionMatrixDisplay\n",
    "\n",
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "27550b76-c498-47fb-9ee6-9633ef3878c8",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_features = df['Clean_Text']\n",
    "y_labels = df['Emotion']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "4b61ac80-e6ad-4429-96b0-f2860d9630f9",
   "metadata": {},
   "outputs": [],
   "source": [
    "cv = CountVectorizer()\n",
    "X = cv.fit_transform(X_features)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "c8b157e3-b9ed-40d6-bf93-4d18bc58ed64",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['10', '100', '11', ..., 'zombie', 'zone', 'zzap'], dtype=object)"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cv.get_feature_names_out()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "3fe43d8f-14ea-4f70-8ac7-3a6edd825d15",
   "metadata": {},
   "outputs": [],
   "source": [
    "#To dense array (numpy)\n",
    "#X.toarray()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "5abbf49e-78b3-495b-8bc4-7fddd26cb2e0",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X, y_labels, test_size = 0.3, random_state = 42)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "46bb7dc0-4d98-48b1-ac57-48b45e084738",
   "metadata": {},
   "source": [
    "## Building our Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "f20ea8d2-10d0-42dd-8bbe-48d7f4aa8036",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "MultinomialNB()"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Naive Bayes Model\n",
    "nb_model = MultinomialNB()\n",
    "nb_model.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "23374dd1-87bc-470f-be9d-73b65e23ac39",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.56286836935167"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nb_model.score(X_test, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "ae326f60-6ed6-4dd6-bbe7-dc75ac7a0c7d",
   "metadata": {},
   "outputs": [],
   "source": [
    "y_predict_for_nb = nb_model.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "6c9c9566-fdd7-4bd1-8cb1-202248fa48ca",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['anger', 'joy', 'sadness', ..., 'sadness', 'neutral', 'anger'],\n",
       "      dtype='<U7')"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_predict_for_nb"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "18ca3030-b1f9-4e06-a6ef-87fd6982f09c",
   "metadata": {},
   "source": [
    "## Making a Single prediction\n",
    "+ Vextorize our model\n",
    "+ Applu our model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "11d652b3-25fd-488c-8687-ba94b44e9582",
   "metadata": {},
   "outputs": [],
   "source": [
    "my_sample_text = ['I love to code so much']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "ff270e61-e5cb-40d7-8d58-2bfd7c038e39",
   "metadata": {},
   "outputs": [],
   "source": [
    "vect_text = cv.transform(my_sample_text).toarray()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "d543a5d5-319b-44e6-a901-5c104efb1f7f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['joy'], dtype='<U7')"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nb_model.predict(vect_text)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "a388f65d-b3a9-429b-b71c-111ad47dc38d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.18231013, 0.03509153, 0.39927742, 0.03594598, 0.34737494]])"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#check for the prediction probability(confidence score)\n",
    "nb_model.predict_proba(vect_text)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "a4a8863b-5abf-405d-94fa-d7b2c37e33b5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['anger', 'fear', 'joy', 'neutral', 'sadness'], dtype='<U7')"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nb_model.classes_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "e073a8c9-25db-4a14-a715-db28b3deab8e",
   "metadata": {},
   "outputs": [],
   "source": [
    "#putting it into a function\n",
    "def predict_emotion(my_sample_text, model):\n",
    "    myvect = cv.transform(my_sample_text).toarray()\n",
    "    prediction = model.predict(myvect)\n",
    "    pred_proba = model.predict_proba(myvect)\n",
    "    pred_percentage_for_all = dict(zip(model.classes_, pred_proba[0]))\n",
    "    print(\"Prediction:{}, Prediction Score: {}\".format(prediction[0], np.max(pred_proba)))\n",
    "    return pred_percentage_for_all"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "24d90cf8-48df-48c5-b1f6-79d4ac48ccd0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Prediction:joy, Prediction Score: 0.39927742481874323\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "{'anger': 0.18231013402083787,\n",
       " 'fear': 0.0350915294442945,\n",
       " 'joy': 0.39927742481874323,\n",
       " 'neutral': 0.035945976648399394,\n",
       " 'sadness': 0.3473749350677273}"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predict_emotion(my_sample_text, nb_model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "2c5d4d37-f6fd-4edd-a80d-98edf93a3661",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Prediction:anger, Prediction Score: 0.8379712070414974\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "{'anger': 0.8379712070414974,\n",
       " 'fear': 0.062236220082417436,\n",
       " 'joy': 0.03515718121224291,\n",
       " 'neutral': 0.04831529094404338,\n",
       " 'sadness': 0.016320100719799557}"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predict_emotion(['She does not like running all day'], nb_model)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9c4ea554-5663-4242-8490-4deb04639bad",
   "metadata": {},
   "source": [
    "## Model Evaluation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "54329017-93cb-4d14-bc98-ec4fd223a31d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "       anger       0.47      0.57      0.52       201\n",
      "        fear       0.65      0.64      0.65       216\n",
      "         joy       0.63      0.60      0.61       223\n",
      "     neutral       0.44      0.37      0.40       160\n",
      "     sadness       0.59      0.58      0.58       218\n",
      "\n",
      "    accuracy                           0.56      1018\n",
      "   macro avg       0.56      0.55      0.55      1018\n",
      "weighted avg       0.56      0.56      0.56      1018\n",
      "\n"
     ]
    }
   ],
   "source": [
    "#Classification\n",
    "print(classification_report(y_test, y_predict_for_nb))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "7d550be8-5684-41fe-8e8e-62dda3f9eb42",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[115,  22,  17,  13,  34],\n",
       "       [ 26, 139,  18,  15,  18],\n",
       "       [ 18,  12, 134,  36,  23],\n",
       "       [ 47,  18,  24,  59,  12],\n",
       "       [ 37,  23,  21,  11, 126]], dtype=int64)"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Confusion matrix\n",
    "confusion_matrix(y_test, y_predict_for_nb)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "06fa4778-6ee6-4b4b-b04e-b6aa5379333e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<sklearn.metrics._plot.confusion_matrix.ConfusionMatrixDisplay at 0x16875e74bb0>"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Plot confusion matrix\n",
    "ConfusionMatrixDisplay.from_estimator(nb_model,X_test, y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b6ed3393-25b8-48c4-b1b9-558a9ec33b34",
   "metadata": {},
   "source": [
    "## Save Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "e7bf7c79-1761-4631-80e6-93e802604339",
   "metadata": {},
   "outputs": [],
   "source": [
    "import joblib"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "9a8487e5-5b52-4e1d-989e-3f8be12f153e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<function BufferedWriter.close>"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_file = open('Emotion_classifier_nb_model_12_July_2022.pkl','wb')\n",
    "joblib.dump(nb_model, model_file)\n",
    "model_file.close"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "04ea0a20-b9d0-40b9-b85c-fe7d403a840e",
   "metadata": {},
   "source": [
    "## Model Interpretation\n",
    "+ Eli5 (You can't use Eli5 to interpret when you use Naive Bayes - Use Logistic regression)\n",
    "+ Lime\n",
    "+ Shap"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "acdf2e03-7411-4b49-bce1-0105db62a973",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Creating a Pipeline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "a790d00b-f64c-4b93-8e94-4f52b28aca76",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LogisticRegression()"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# LogisticRegression\n",
    "lr_model = LogisticRegression()\n",
    "lr_model.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "26843a71-d131-40ce-b78d-31069e28b16f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.5736738703339882"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Accuracy\n",
    "lr_model.score(X_test, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "045ee1a1-3d79-4fbf-b5ea-ee96157fb70c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Prediction:joy, Prediction Score: 0.4395538897059083\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "{'anger': 0.11017605341212587,\n",
       " 'fear': 0.10077418215877317,\n",
       " 'joy': 0.4395538897059083,\n",
       " 'neutral': 0.20792651240931587,\n",
       " 'sadness': 0.1415693623138769}"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Single prediction\n",
    "predict_emotion(my_sample_text, lr_model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "ae18e193-b7bd-4e5d-9d11-56eb20d0684e",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Interpret model\n",
    "import eli5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "05f1a224-8d19-4106-9c79-2b7572943212",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "        \n",
       "\n",
       "    \n",
       "        <table class=\"eli5-weights-wrapper\" style=\"border-collapse: collapse; border: none; margin-bottom: 1.5em;\">\n",
       "            <tr>\n",
       "                \n",
       "                    <td style=\"padding: 0.5em; border: 1px solid black; text-align: center;\">\n",
       "                        <b>\n",
       "    \n",
       "        y=anger\n",
       "    \n",
       "</b>\n",
       "\n",
       "top features\n",
       "                    </td>\n",
       "                \n",
       "                    <td style=\"padding: 0.5em; border: 1px solid black; text-align: center;\">\n",
       "                        <b>\n",
       "    \n",
       "        y=fear\n",
       "    \n",
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       "\n",
       "top features\n",
       "                    </td>\n",
       "                \n",
       "                    <td style=\"padding: 0.5em; border: 1px solid black; text-align: center;\">\n",
       "                        <b>\n",
       "    \n",
       "        y=joy\n",
       "    \n",
       "</b>\n",
       "\n",
       "top features\n",
       "                    </td>\n",
       "                \n",
       "                    <td style=\"padding: 0.5em; border: 1px solid black; text-align: center;\">\n",
       "                        <b>\n",
       "    \n",
       "        y=neutral\n",
       "    \n",
       "</b>\n",
       "\n",
       "top features\n",
       "                    </td>\n",
       "                \n",
       "                    <td style=\"padding: 0.5em; border: 1px solid black; text-align: center;\">\n",
       "                        <b>\n",
       "    \n",
       "        y=sadness\n",
       "    \n",
       "</b>\n",
       "\n",
       "top features\n",
       "                    </td>\n",
       "                \n",
       "            </tr>\n",
       "            <tr>\n",
       "                \n",
       "                    \n",
       "                        <td style=\"padding: 0px; border: 1px solid black; vertical-align: top;\">\n",
       "                            \n",
       "                                \n",
       "                                    \n",
       "                                    \n",
       "    \n",
       "    <table class=\"eli5-weights\"\n",
       "           style=\"border-collapse: collapse; border: none; margin-top: 0em; table-layout: auto; width: 100%;\">\n",
       "        <thead>\n",
       "        <tr style=\"border: none;\">\n",
       "            \n",
       "                <th style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\" title=\"Feature weights. Note that weights do not account for feature value scales, so if feature values have different scales, features with highest weights might not be the most important.\">\n",
       "                    Weight<sup>?</sup>\n",
       "                </th>\n",
       "            \n",
       "            <th style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">Feature</th>\n",
       "            \n",
       "        </tr>\n",
       "        </thead>\n",
       "        <tbody>\n",
       "        \n",
       "            <tr style=\"background-color: hsl(120, 100.00%, 81.67%); border: none;\">\n",
       "    <td style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\">\n",
       "        +2.397\n",
       "    </td>\n",
       "    <td style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">\n",
       "        x302\n",
       "    </td>\n",
       "    \n",
       "</tr>\n",
       "        \n",
       "            <tr style=\"background-color: hsl(120, 100.00%, 83.93%); border: none;\">\n",
       "    <td style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\">\n",
       "        +1.986\n",
       "    </td>\n",
       "    <td style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">\n",
       "        x297\n",
       "    </td>\n",
       "    \n",
       "</tr>\n",
       "        \n",
       "            <tr style=\"background-color: hsl(120, 100.00%, 84.39%); border: none;\">\n",
       "    <td style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\">\n",
       "        +1.906\n",
       "    </td>\n",
       "    <td style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">\n",
       "        x2324\n",
       "    </td>\n",
       "    \n",
       "</tr>\n",
       "        \n",
       "            <tr style=\"background-color: hsl(120, 100.00%, 85.86%); border: none;\">\n",
       "    <td style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\">\n",
       "        +1.655\n",
       "    </td>\n",
       "    <td style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">\n",
       "        x3344\n",
       "    </td>\n",
       "    \n",
       "</tr>\n",
       "        \n",
       "            <tr style=\"background-color: hsl(120, 100.00%, 86.02%); border: none;\">\n",
       "    <td style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\">\n",
       "        +1.629\n",
       "    </td>\n",
       "    <td style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">\n",
       "        x2865\n",
       "    </td>\n",
       "    \n",
       "</tr>\n",
       "        \n",
       "            <tr style=\"background-color: hsl(120, 100.00%, 86.36%); border: none;\">\n",
       "    <td style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\">\n",
       "        +1.572\n",
       "    </td>\n",
       "    <td style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">\n",
       "        x1957\n",
       "    </td>\n",
       "    \n",
       "</tr>\n",
       "        \n",
       "            <tr style=\"background-color: hsl(120, 100.00%, 86.95%); border: none;\">\n",
       "    <td style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\">\n",
       "        +1.476\n",
       "    </td>\n",
       "    <td style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">\n",
       "        x3149\n",
       "    </td>\n",
       "    \n",
       "</tr>\n",
       "        \n",
       "            <tr style=\"background-color: hsl(120, 100.00%, 87.46%); border: none;\">\n",
       "    <td style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\">\n",
       "        +1.394\n",
       "    </td>\n",
       "    <td style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">\n",
       "        x137\n",
       "    </td>\n",
       "    \n",
       "</tr>\n",
       "        \n",
       "            <tr style=\"background-color: hsl(120, 100.00%, 87.83%); border: none;\">\n",
       "    <td style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\">\n",
       "        +1.335\n",
       "    </td>\n",
       "    <td style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">\n",
       "        x6058\n",
       "    </td>\n",
       "    \n",
       "</tr>\n",
       "        \n",
       "            <tr style=\"background-color: hsl(120, 100.00%, 87.86%); border: none;\">\n",
       "    <td style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\">\n",
       "        +1.332\n",
       "    </td>\n",
       "    <td style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">\n",
       "        x4613\n",
       "    </td>\n",
       "    \n",
       "</tr>\n",
       "        \n",
       "            <tr style=\"background-color: hsl(120, 100.00%, 88.60%); border: none;\">\n",
       "    <td style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\">\n",
       "        +1.216\n",
       "    </td>\n",
       "    <td style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">\n",
       "        x4716\n",
       "    </td>\n",
       "    \n",
       "</tr>\n",
       "        \n",
       "            <tr style=\"background-color: hsl(120, 100.00%, 88.62%); border: none;\">\n",
       "    <td style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\">\n",
       "        +1.214\n",
       "    </td>\n",
       "    <td style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">\n",
       "        x3518\n",
       "    </td>\n",
       "    \n",
       "</tr>\n",
       "        \n",
       "            <tr style=\"background-color: hsl(120, 100.00%, 88.67%); border: none;\">\n",
       "    <td style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\">\n",
       "        +1.205\n",
       "    </td>\n",
       "    <td style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">\n",
       "        x2777\n",
       "    </td>\n",
       "    \n",
       "</tr>\n",
       "        \n",
       "            <tr style=\"background-color: hsl(120, 100.00%, 89.14%); border: none;\">\n",
       "    <td style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\">\n",
       "        +1.135\n",
       "    </td>\n",
       "    <td style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">\n",
       "        x4385\n",
       "    </td>\n",
       "    \n",
       "</tr>\n",
       "        \n",
       "            <tr style=\"background-color: hsl(120, 100.00%, 89.22%); border: none;\">\n",
       "    <td style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\">\n",
       "        +1.123\n",
       "    </td>\n",
       "    <td style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">\n",
       "        x5980\n",
       "    </td>\n",
       "    \n",
       "</tr>\n",
       "        \n",
       "            <tr style=\"background-color: hsl(120, 100.00%, 89.24%); border: none;\">\n",
       "    <td style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\">\n",
       "        +1.120\n",
       "    </td>\n",
       "    <td style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">\n",
       "        x851\n",
       "    </td>\n",
       "    \n",
       "</tr>\n",
       "        \n",
       "            <tr style=\"background-color: hsl(120, 100.00%, 89.45%); border: none;\">\n",
       "    <td style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\">\n",
       "        +1.088\n",
       "    </td>\n",
       "    <td style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">\n",
       "        x1344\n",
       "    </td>\n",
       "    \n",
       "</tr>\n",
       "        \n",
       "            <tr style=\"background-color: hsl(120, 100.00%, 89.56%); border: none;\">\n",
       "    <td style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\">\n",
       "        +1.073\n",
       "    </td>\n",
       "    <td style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">\n",
       "        x1958\n",
       "    </td>\n",
       "    \n",
       "</tr>\n",
       "        \n",
       "            <tr style=\"background-color: hsl(120, 100.00%, 89.65%); border: none;\">\n",
       "    <td style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\">\n",
       "        +1.060\n",
       "    </td>\n",
       "    <td style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">\n",
       "        x3253\n",
       "    </td>\n",
       "    \n",
       "</tr>\n",
       "        \n",
       "        \n",
       "            <tr style=\"background-color: hsl(120, 100.00%, 89.65%); border: none;\">\n",
       "                <td colspan=\"2\" style=\"padding: 0 0.5em 0 0.5em; text-align: center; border: none; white-space: nowrap;\">\n",
       "                    <i>&hellip; 1676 more positive &hellip;</i>\n",
       "                </td>\n",
       "            </tr>\n",
       "        \n",
       "\n",
       "        \n",
       "            <tr style=\"background-color: hsl(0, 100.00%, 89.44%); border: none;\">\n",
       "                <td colspan=\"2\" style=\"padding: 0 0.5em 0 0.5em; text-align: center; border: none; white-space: nowrap;\">\n",
       "                    <i>&hellip; 3389 more negative &hellip;</i>\n",
       "                </td>\n",
       "            </tr>\n",
       "        \n",
       "        \n",
       "            <tr style=\"background-color: hsl(0, 100.00%, 89.44%); border: none;\">\n",
       "    <td style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\">\n",
       "        -1.090\n",
       "    </td>\n",
       "    <td style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">\n",
       "        x195\n",
       "    </td>\n",
       "    \n",
       "</tr>\n",
       "        \n",
       "\n",
       "        </tbody>\n",
       "    </table>\n",
       "\n",
       "                                \n",
       "                            \n",
       "                        </td>\n",
       "                    \n",
       "                        <td style=\"padding: 0px; border: 1px solid black; vertical-align: top;\">\n",
       "                            \n",
       "                                \n",
       "                                    \n",
       "                                    \n",
       "    \n",
       "    <table class=\"eli5-weights\"\n",
       "           style=\"border-collapse: collapse; border: none; margin-top: 0em; table-layout: auto; width: 100%;\">\n",
       "        <thead>\n",
       "        <tr style=\"border: none;\">\n",
       "            \n",
       "                <th style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\" title=\"Feature weights. Note that weights do not account for feature value scales, so if feature values have different scales, features with highest weights might not be the most important.\">\n",
       "                    Weight<sup>?</sup>\n",
       "                </th>\n",
       "            \n",
       "            <th style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">Feature</th>\n",
       "            \n",
       "        </tr>\n",
       "        </thead>\n",
       "        <tbody>\n",
       "        \n",
       "            <tr style=\"background-color: hsl(120, 100.00%, 82.01%); border: none;\">\n",
       "    <td style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\">\n",
       "        +2.335\n",
       "    </td>\n",
       "    <td style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">\n",
       "        x2737\n",
       "    </td>\n",
       "    \n",
       "</tr>\n",
       "        \n",
       "            <tr style=\"background-color: hsl(120, 100.00%, 82.42%); border: none;\">\n",
       "    <td style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\">\n",
       "        +2.260\n",
       "    </td>\n",
       "    <td style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">\n",
       "        x2293\n",
       "    </td>\n",
       "    \n",
       "</tr>\n",
       "        \n",
       "            <tr style=\"background-color: hsl(120, 100.00%, 82.98%); border: none;\">\n",
       "    <td style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\">\n",
       "        +2.157\n",
       "    </td>\n",
       "    <td style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">\n",
       "        x3706\n",
       "    </td>\n",
       "    \n",
       "</tr>\n",
       "        \n",
       "            <tr style=\"background-color: hsl(120, 100.00%, 83.32%); border: none;\">\n",
       "    <td style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\">\n",
       "        +2.095\n",
       "    </td>\n",
       "    <td style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">\n",
       "        x195\n",
       "    </td>\n",
       "    \n",
       "</tr>\n",
       "        \n",
       "            <tr style=\"background-color: hsl(120, 100.00%, 83.94%); border: none;\">\n",
       "    <td style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\">\n",
       "        +1.985\n",
       "    </td>\n",
       "    <td style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">\n",
       "        x1619\n",
       "    </td>\n",
       "    \n",
       "</tr>\n",
       "        \n",
       "            <tr style=\"background-color: hsl(120, 100.00%, 84.06%); border: none;\">\n",
       "    <td style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\">\n",
       "        +1.964\n",
       "    </td>\n",
       "    <td style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">\n",
       "        x4816\n",
       "    </td>\n",
       "    \n",
       "</tr>\n",
       "        \n",
       "            <tr style=\"background-color: hsl(120, 100.00%, 84.26%); border: none;\">\n",
       "    <td style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\">\n",
       "        +1.929\n",
       "    </td>\n",
       "    <td style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">\n",
       "        x2086\n",
       "    </td>\n",
       "    \n",
       "</tr>\n",
       "        \n",
       "            <tr style=\"background-color: hsl(120, 100.00%, 84.86%); border: none;\">\n",
       "    <td style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\">\n",
       "        +1.824\n",
       "    </td>\n",
       "    <td style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">\n",
       "        x328\n",
       "    </td>\n",
       "    \n",
       "</tr>\n",
       "        \n",
       "            <tr style=\"background-color: hsl(120, 100.00%, 86.44%); border: none;\">\n",
       "    <td style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\">\n",
       "        +1.559\n",
       "    </td>\n",
       "    <td style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">\n",
       "        x3728\n",
       "    </td>\n",
       "    \n",
       "</tr>\n",
       "        \n",
       "            <tr style=\"background-color: hsl(120, 100.00%, 87.25%); border: none;\">\n",
       "    <td style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\">\n",
       "        +1.428\n",
       "    </td>\n",
       "    <td style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">\n",
       "        x1176\n",
       "    </td>\n",
       "    \n",
       "</tr>\n",
       "        \n",
       "            <tr style=\"background-color: hsl(120, 100.00%, 87.63%); border: none;\">\n",
       "    <td style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\">\n",
       "        +1.367\n",
       "    </td>\n",
       "    <td style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">\n",
       "        x1411\n",
       "    </td>\n",
       "    \n",
       "</tr>\n",
       "        \n",
       "            <tr style=\"background-color: hsl(120, 100.00%, 88.05%); border: none;\">\n",
       "    <td style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\">\n",
       "        +1.302\n",
       "    </td>\n",
       "    <td style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">\n",
       "        x2120\n",
       "    </td>\n",
       "    \n",
       "</tr>\n",
       "        \n",
       "            <tr style=\"background-color: hsl(120, 100.00%, 88.24%); border: none;\">\n",
       "    <td style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\">\n",
       "        +1.272\n",
       "    </td>\n",
       "    <td style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">\n",
       "        x1630\n",
       "    </td>\n",
       "    \n",
       "</tr>\n",
       "        \n",
       "            <tr style=\"background-color: hsl(120, 100.00%, 89.22%); border: none;\">\n",
       "    <td style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\">\n",
       "        +1.123\n",
       "    </td>\n",
       "    <td style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">\n",
       "        x1785\n",
       "    </td>\n",
       "    \n",
       "</tr>\n",
       "        \n",
       "            <tr style=\"background-color: hsl(120, 100.00%, 89.27%); border: none;\">\n",
       "    <td style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\">\n",
       "        +1.117\n",
       "    </td>\n",
       "    <td style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">\n",
       "        x5645\n",
       "    </td>\n",
       "    \n",
       "</tr>\n",
       "        \n",
       "            <tr style=\"background-color: hsl(120, 100.00%, 89.30%); border: none;\">\n",
       "    <td style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\">\n",
       "        +1.111\n",
       "    </td>\n",
       "    <td style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">\n",
       "        x5034\n",
       "    </td>\n",
       "    \n",
       "</tr>\n",
       "        \n",
       "            <tr style=\"background-color: hsl(120, 100.00%, 89.55%); border: none;\">\n",
       "    <td style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\">\n",
       "        +1.074\n",
       "    </td>\n",
       "    <td style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">\n",
       "        x2032\n",
       "    </td>\n",
       "    \n",
       "</tr>\n",
       "        \n",
       "            <tr style=\"background-color: hsl(120, 100.00%, 89.62%); border: none;\">\n",
       "    <td style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\">\n",
       "        +1.064\n",
       "    </td>\n",
       "    <td style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">\n",
       "        x1950\n",
       "    </td>\n",
       "    \n",
       "</tr>\n",
       "        \n",
       "            <tr style=\"background-color: hsl(120, 100.00%, 90.14%); border: none;\">\n",
       "    <td style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\">\n",
       "        +0.988\n",
       "    </td>\n",
       "    <td style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">\n",
       "        x234\n",
       "    </td>\n",
       "    \n",
       "</tr>\n",
       "        \n",
       "            <tr style=\"background-color: hsl(120, 100.00%, 90.22%); border: none;\">\n",
       "    <td style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\">\n",
       "        +0.977\n",
       "    </td>\n",
       "    <td style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">\n",
       "        x2736\n",
       "    </td>\n",
       "    \n",
       "</tr>\n",
       "        \n",
       "        \n",
       "            <tr style=\"background-color: hsl(120, 100.00%, 90.22%); border: none;\">\n",
       "                <td colspan=\"2\" style=\"padding: 0 0.5em 0 0.5em; text-align: center; border: none; white-space: nowrap;\">\n",
       "                    <i>&hellip; 1828 more positive &hellip;</i>\n",
       "                </td>\n",
       "            </tr>\n",
       "        \n",
       "\n",
       "        \n",
       "            <tr style=\"background-color: hsl(0, 100.00%, 80.00%); border: none;\">\n",
       "                <td colspan=\"2\" style=\"padding: 0 0.5em 0 0.5em; text-align: center; border: none; white-space: nowrap;\">\n",
       "                    <i>&hellip; 3237 more negative &hellip;</i>\n",
       "                </td>\n",
       "            </tr>\n",
       "        \n",
       "        \n",
       "\n",
       "        </tbody>\n",
       "    </table>\n",
       "\n",
       "                                \n",
       "                            \n",
       "                        </td>\n",
       "                    \n",
       "                        <td style=\"padding: 0px; border: 1px solid black; vertical-align: top;\">\n",
       "                            \n",
       "                                \n",
       "                                    \n",
       "                                    \n",
       "    \n",
       "    <table class=\"eli5-weights\"\n",
       "           style=\"border-collapse: collapse; border: none; margin-top: 0em; table-layout: auto; width: 100%;\">\n",
       "        <thead>\n",
       "        <tr style=\"border: none;\">\n",
       "            \n",
       "                <th style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\" title=\"Feature weights. Note that weights do not account for feature value scales, so if feature values have different scales, features with highest weights might not be the most important.\">\n",
       "                    Weight<sup>?</sup>\n",
       "                </th>\n",
       "            \n",
       "            <th style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">Feature</th>\n",
       "            \n",
       "        </tr>\n",
       "        </thead>\n",
       "        <tbody>\n",
       "        \n",
       "            <tr style=\"background-color: hsl(120, 100.00%, 83.28%); border: none;\">\n",
       "    <td style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\">\n",
       "        +2.103\n",
       "    </td>\n",
       "    <td style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">\n",
       "        x2578\n",
       "    </td>\n",
       "    \n",
       "</tr>\n",
       "        \n",
       "            <tr style=\"background-color: hsl(120, 100.00%, 84.62%); border: none;\">\n",
       "    <td style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\">\n",
       "        +1.867\n",
       "    </td>\n",
       "    <td style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">\n",
       "        x3037\n",
       "    </td>\n",
       "    \n",
       "</tr>\n",
       "        \n",
       "            <tr style=\"background-color: hsl(120, 100.00%, 84.67%); border: none;\">\n",
       "    <td style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\">\n",
       "        +1.857\n",
       "    </td>\n",
       "    <td style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">\n",
       "        x1965\n",
       "    </td>\n",
       "    \n",
       "</tr>\n",
       "        \n",
       "            <tr style=\"background-color: hsl(120, 100.00%, 85.14%); border: none;\">\n",
       "    <td style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\">\n",
       "        +1.777\n",
       "    </td>\n",
       "    <td style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">\n",
       "        x1963\n",
       "    </td>\n",
       "    \n",
       "</tr>\n",
       "        \n",
       "            <tr style=\"background-color: hsl(120, 100.00%, 85.38%); border: none;\">\n",
       "    <td style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\">\n",
       "        +1.736\n",
       "    </td>\n",
       "    <td style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">\n",
       "        x3999\n",
       "    </td>\n",
       "    \n",
       "</tr>\n",
       "        \n",
       "            <tr style=\"background-color: hsl(120, 100.00%, 86.26%); border: none;\">\n",
       "    <td style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\">\n",
       "        +1.588\n",
       "    </td>\n",
       "    <td style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">\n",
       "        x1799\n",
       "    </td>\n",
       "    \n",
       "</tr>\n",
       "        \n",
       "            <tr style=\"background-color: hsl(120, 100.00%, 86.43%); border: none;\">\n",
       "    <td style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\">\n",
       "        +1.561\n",
       "    </td>\n",
       "    <td style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">\n",
       "        x1477\n",
       "    </td>\n",
       "    \n",
       "</tr>\n",
       "        \n",
       "            <tr style=\"background-color: hsl(120, 100.00%, 86.55%); border: none;\">\n",
       "    <td style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\">\n",
       "        +1.541\n",
       "    </td>\n",
       "    <td style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">\n",
       "        x4137\n",
       "    </td>\n",
       "    \n",
       "</tr>\n",
       "        \n",
       "            <tr style=\"background-color: hsl(120, 100.00%, 86.79%); border: none;\">\n",
       "    <td style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\">\n",
       "        +1.502\n",
       "    </td>\n",
       "    <td style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">\n",
       "        x729\n",
       "    </td>\n",
       "    \n",
       "</tr>\n",
       "        \n",
       "            <tr style=\"background-color: hsl(120, 100.00%, 87.05%); border: none;\">\n",
       "    <td style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\">\n",
       "        +1.459\n",
       "    </td>\n",
       "    <td style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">\n",
       "        x1476\n",
       "    </td>\n",
       "    \n",
       "</tr>\n",
       "        \n",
       "            <tr style=\"background-color: hsl(120, 100.00%, 87.74%); border: none;\">\n",
       "    <td style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\">\n",
       "        +1.351\n",
       "    </td>\n",
       "    <td style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">\n",
       "        x4647\n",
       "    </td>\n",
       "    \n",
       "</tr>\n",
       "        \n",
       "            <tr style=\"background-color: hsl(120, 100.00%, 87.96%); border: none;\">\n",
       "    <td style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\">\n",
       "        +1.315\n",
       "    </td>\n",
       "    <td style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">\n",
       "        x2485\n",
       "    </td>\n",
       "    \n",
       "</tr>\n",
       "        \n",
       "            <tr style=\"background-color: hsl(120, 100.00%, 88.09%); border: none;\">\n",
       "    <td style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\">\n",
       "        +1.294\n",
       "    </td>\n",
       "    <td style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">\n",
       "        x283\n",
       "    </td>\n",
       "    \n",
       "</tr>\n",
       "        \n",
       "            <tr style=\"background-color: hsl(120, 100.00%, 88.18%); border: none;\">\n",
       "    <td style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\">\n",
       "        +1.281\n",
       "    </td>\n",
       "    <td style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">\n",
       "        x3719\n",
       "    </td>\n",
       "    \n",
       "</tr>\n",
       "        \n",
       "            <tr style=\"background-color: hsl(120, 100.00%, 88.49%); border: none;\">\n",
       "    <td style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\">\n",
       "        +1.233\n",
       "    </td>\n",
       "    <td style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">\n",
       "        x5593\n",
       "    </td>\n",
       "    \n",
       "</tr>\n",
       "        \n",
       "            <tr style=\"background-color: hsl(120, 100.00%, 88.89%); border: none;\">\n",
       "    <td style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\">\n",
       "        +1.173\n",
       "    </td>\n",
       "    <td style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">\n",
       "        x3205\n",
       "    </td>\n",
       "    \n",
       "</tr>\n",
       "        \n",
       "            <tr style=\"background-color: hsl(120, 100.00%, 89.08%); border: none;\">\n",
       "    <td style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\">\n",
       "        +1.144\n",
       "    </td>\n",
       "    <td style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">\n",
       "        x1030\n",
       "    </td>\n",
       "    \n",
       "</tr>\n",
       "        \n",
       "            <tr style=\"background-color: hsl(120, 100.00%, 89.17%); border: none;\">\n",
       "    <td style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\">\n",
       "        +1.131\n",
       "    </td>\n",
       "    <td style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">\n",
       "        x3913\n",
       "    </td>\n",
       "    \n",
       "</tr>\n",
       "        \n",
       "            <tr style=\"background-color: hsl(120, 100.00%, 89.20%); border: none;\">\n",
       "    <td style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\">\n",
       "        +1.126\n",
       "    </td>\n",
       "    <td style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">\n",
       "        x4052\n",
       "    </td>\n",
       "    \n",
       "</tr>\n",
       "        \n",
       "            <tr style=\"background-color: hsl(120, 100.00%, 89.20%); border: none;\">\n",
       "    <td style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\">\n",
       "        +1.126\n",
       "    </td>\n",
       "    <td style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">\n",
       "        x119\n",
       "    </td>\n",
       "    \n",
       "</tr>\n",
       "        \n",
       "        \n",
       "            <tr style=\"background-color: hsl(120, 100.00%, 89.20%); border: none;\">\n",
       "                <td colspan=\"2\" style=\"padding: 0 0.5em 0 0.5em; text-align: center; border: none; white-space: nowrap;\">\n",
       "                    <i>&hellip; 1287 more positive &hellip;</i>\n",
       "                </td>\n",
       "            </tr>\n",
       "        \n",
       "\n",
       "        \n",
       "            <tr style=\"background-color: hsl(0, 100.00%, 80.00%); border: none;\">\n",
       "                <td colspan=\"2\" style=\"padding: 0 0.5em 0 0.5em; text-align: center; border: none; white-space: nowrap;\">\n",
       "                    <i>&hellip; 3778 more negative &hellip;</i>\n",
       "                </td>\n",
       "            </tr>\n",
       "        \n",
       "        \n",
       "\n",
       "        </tbody>\n",
       "    </table>\n",
       "\n",
       "                                \n",
       "                            \n",
       "                        </td>\n",
       "                    \n",
       "                        <td style=\"padding: 0px; border: 1px solid black; vertical-align: top;\">\n",
       "                            \n",
       "                                \n",
       "                                    \n",
       "                                    \n",
       "    \n",
       "    <table class=\"eli5-weights\"\n",
       "           style=\"border-collapse: collapse; border: none; margin-top: 0em; table-layout: auto; width: 100%;\">\n",
       "        <thead>\n",
       "        <tr style=\"border: none;\">\n",
       "            \n",
       "                <th style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\" title=\"Feature weights. Note that weights do not account for feature value scales, so if feature values have different scales, features with highest weights might not be the most important.\">\n",
       "                    Weight<sup>?</sup>\n",
       "                </th>\n",
       "            \n",
       "            <th style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">Feature</th>\n",
       "            \n",
       "        </tr>\n",
       "        </thead>\n",
       "        <tbody>\n",
       "        \n",
       "            <tr style=\"background-color: hsl(120, 100.00%, 87.82%); border: none;\">\n",
       "    <td style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\">\n",
       "        +1.337\n",
       "    </td>\n",
       "    <td style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">\n",
       "        &lt;BIAS&gt;\n",
       "    </td>\n",
       "    \n",
       "</tr>\n",
       "        \n",
       "            <tr style=\"background-color: hsl(120, 100.00%, 89.66%); border: none;\">\n",
       "    <td style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\">\n",
       "        +1.058\n",
       "    </td>\n",
       "    <td style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">\n",
       "        x2659\n",
       "    </td>\n",
       "    \n",
       "</tr>\n",
       "        \n",
       "            <tr style=\"background-color: hsl(120, 100.00%, 90.11%); border: none;\">\n",
       "    <td style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\">\n",
       "        +0.993\n",
       "    </td>\n",
       "    <td style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">\n",
       "        x4272\n",
       "    </td>\n",
       "    \n",
       "</tr>\n",
       "        \n",
       "            <tr style=\"background-color: hsl(120, 100.00%, 90.32%); border: none;\">\n",
       "    <td style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\">\n",
       "        +0.963\n",
       "    </td>\n",
       "    <td style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">\n",
       "        x6248\n",
       "    </td>\n",
       "    \n",
       "</tr>\n",
       "        \n",
       "        \n",
       "            <tr style=\"background-color: hsl(120, 100.00%, 90.32%); border: none;\">\n",
       "                <td colspan=\"2\" style=\"padding: 0 0.5em 0 0.5em; text-align: center; border: none; white-space: nowrap;\">\n",
       "                    <i>&hellip; 532 more positive &hellip;</i>\n",
       "                </td>\n",
       "            </tr>\n",
       "        \n",
       "\n",
       "        \n",
       "            <tr style=\"background-color: hsl(0, 100.00%, 90.47%); border: none;\">\n",
       "                <td colspan=\"2\" style=\"padding: 0 0.5em 0 0.5em; text-align: center; border: none; white-space: nowrap;\">\n",
       "                    <i>&hellip; 4533 more negative &hellip;</i>\n",
       "                </td>\n",
       "            </tr>\n",
       "        \n",
       "        \n",
       "            <tr style=\"background-color: hsl(0, 100.00%, 90.47%); border: none;\">\n",
       "    <td style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\">\n",
       "        -0.942\n",
       "    </td>\n",
       "    <td style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">\n",
       "        x2291\n",
       "    </td>\n",
       "    \n",
       "</tr>\n",
       "        \n",
       "            <tr style=\"background-color: hsl(0, 100.00%, 90.39%); border: none;\">\n",
       "    <td style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\">\n",
       "        -0.954\n",
       "    </td>\n",
       "    <td style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">\n",
       "        x5576\n",
       "    </td>\n",
       "    \n",
       "</tr>\n",
       "        \n",
       "            <tr style=\"background-color: hsl(0, 100.00%, 90.37%); border: none;\">\n",
       "    <td style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\">\n",
       "        -0.957\n",
       "    </td>\n",
       "    <td style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">\n",
       "        x3706\n",
       "    </td>\n",
       "    \n",
       "</tr>\n",
       "        \n",
       "            <tr style=\"background-color: hsl(0, 100.00%, 90.28%); border: none;\">\n",
       "    <td style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\">\n",
       "        -0.970\n",
       "    </td>\n",
       "    <td style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">\n",
       "        x753\n",
       "    </td>\n",
       "    \n",
       "</tr>\n",
       "        \n",
       "            <tr style=\"background-color: hsl(0, 100.00%, 90.27%); border: none;\">\n",
       "    <td style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\">\n",
       "        -0.971\n",
       "    </td>\n",
       "    <td style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">\n",
       "        x5705\n",
       "    </td>\n",
       "    \n",
       "</tr>\n",
       "        \n",
       "            <tr style=\"background-color: hsl(0, 100.00%, 89.78%); border: none;\">\n",
       "    <td style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\">\n",
       "        -1.041\n",
       "    </td>\n",
       "    <td style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">\n",
       "        x2383\n",
       "    </td>\n",
       "    \n",
       "</tr>\n",
       "        \n",
       "            <tr style=\"background-color: hsl(0, 100.00%, 89.57%); border: none;\">\n",
       "    <td style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\">\n",
       "        -1.072\n",
       "    </td>\n",
       "    <td style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">\n",
       "        x3019\n",
       "    </td>\n",
       "    \n",
       "</tr>\n",
       "        \n",
       "            <tr style=\"background-color: hsl(0, 100.00%, 89.32%); border: none;\">\n",
       "    <td style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\">\n",
       "        -1.108\n",
       "    </td>\n",
       "    <td style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">\n",
       "        x2026\n",
       "    </td>\n",
       "    \n",
       "</tr>\n",
       "        \n",
       "            <tr style=\"background-color: hsl(0, 100.00%, 89.32%); border: none;\">\n",
       "    <td style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\">\n",
       "        -1.108\n",
       "    </td>\n",
       "    <td style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">\n",
       "        x4808\n",
       "    </td>\n",
       "    \n",
       "</tr>\n",
       "        \n",
       "            <tr style=\"background-color: hsl(0, 100.00%, 89.14%); border: none;\">\n",
       "    <td style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\">\n",
       "        -1.136\n",
       "    </td>\n",
       "    <td style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">\n",
       "        x4832\n",
       "    </td>\n",
       "    \n",
       "</tr>\n",
       "        \n",
       "            <tr style=\"background-color: hsl(0, 100.00%, 89.09%); border: none;\">\n",
       "    <td style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\">\n",
       "        -1.143\n",
       "    </td>\n",
       "    <td style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">\n",
       "        x878\n",
       "    </td>\n",
       "    \n",
       "</tr>\n",
       "        \n",
       "            <tr style=\"background-color: hsl(0, 100.00%, 88.89%); border: none;\">\n",
       "    <td style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\">\n",
       "        -1.173\n",
       "    </td>\n",
       "    <td style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">\n",
       "        x2099\n",
       "    </td>\n",
       "    \n",
       "</tr>\n",
       "        \n",
       "            <tr style=\"background-color: hsl(0, 100.00%, 88.84%); border: none;\">\n",
       "    <td style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\">\n",
       "        -1.180\n",
       "    </td>\n",
       "    <td style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">\n",
       "        x3446\n",
       "    </td>\n",
       "    \n",
       "</tr>\n",
       "        \n",
       "            <tr style=\"background-color: hsl(0, 100.00%, 88.60%); border: none;\">\n",
       "    <td style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\">\n",
       "        -1.216\n",
       "    </td>\n",
       "    <td style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">\n",
       "        x2108\n",
       "    </td>\n",
       "    \n",
       "</tr>\n",
       "        \n",
       "            <tr style=\"background-color: hsl(0, 100.00%, 88.40%); border: none;\">\n",
       "    <td style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\">\n",
       "        -1.247\n",
       "    </td>\n",
       "    <td style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">\n",
       "        x3728\n",
       "    </td>\n",
       "    \n",
       "</tr>\n",
       "        \n",
       "            <tr style=\"background-color: hsl(0, 100.00%, 85.71%); border: none;\">\n",
       "    <td style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\">\n",
       "        -1.680\n",
       "    </td>\n",
       "    <td style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">\n",
       "        x2289\n",
       "    </td>\n",
       "    \n",
       "</tr>\n",
       "        \n",
       "\n",
       "        </tbody>\n",
       "    </table>\n",
       "\n",
       "                                \n",
       "                            \n",
       "                        </td>\n",
       "                    \n",
       "                        <td style=\"padding: 0px; border: 1px solid black; vertical-align: top;\">\n",
       "                            \n",
       "                                \n",
       "                                    \n",
       "                                    \n",
       "    \n",
       "    <table class=\"eli5-weights\"\n",
       "           style=\"border-collapse: collapse; border: none; margin-top: 0em; table-layout: auto; width: 100%;\">\n",
       "        <thead>\n",
       "        <tr style=\"border: none;\">\n",
       "            \n",
       "                <th style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\" title=\"Feature weights. Note that weights do not account for feature value scales, so if feature values have different scales, features with highest weights might not be the most important.\">\n",
       "                    Weight<sup>?</sup>\n",
       "                </th>\n",
       "            \n",
       "            <th style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">Feature</th>\n",
       "            \n",
       "        </tr>\n",
       "        </thead>\n",
       "        <tbody>\n",
       "        \n",
       "            <tr style=\"background-color: hsl(120, 100.00%, 80.00%); border: none;\">\n",
       "    <td style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\">\n",
       "        +2.716\n",
       "    </td>\n",
       "    <td style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">\n",
       "        x5171\n",
       "    </td>\n",
       "    \n",
       "</tr>\n",
       "        \n",
       "            <tr style=\"background-color: hsl(120, 100.00%, 81.45%); border: none;\">\n",
       "    <td style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\">\n",
       "        +2.439\n",
       "    </td>\n",
       "    <td style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">\n",
       "        x1556\n",
       "    </td>\n",
       "    \n",
       "</tr>\n",
       "        \n",
       "            <tr style=\"background-color: hsl(120, 100.00%, 81.50%); border: none;\">\n",
       "    <td style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\">\n",
       "        +2.429\n",
       "    </td>\n",
       "    <td style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">\n",
       "        x4767\n",
       "    </td>\n",
       "    \n",
       "</tr>\n",
       "        \n",
       "            <tr style=\"background-color: hsl(120, 100.00%, 83.76%); border: none;\">\n",
       "    <td style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\">\n",
       "        +2.016\n",
       "    </td>\n",
       "    <td style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">\n",
       "        x3545\n",
       "    </td>\n",
       "    \n",
       "</tr>\n",
       "        \n",
       "            <tr style=\"background-color: hsl(120, 100.00%, 85.90%); border: none;\">\n",
       "    <td style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\">\n",
       "        +1.648\n",
       "    </td>\n",
       "    <td style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">\n",
       "        x1532\n",
       "    </td>\n",
       "    \n",
       "</tr>\n",
       "        \n",
       "            <tr style=\"background-color: hsl(120, 100.00%, 86.00%); border: none;\">\n",
       "    <td style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\">\n",
       "        +1.631\n",
       "    </td>\n",
       "    <td style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">\n",
       "        x1438\n",
       "    </td>\n",
       "    \n",
       "</tr>\n",
       "        \n",
       "            <tr style=\"background-color: hsl(120, 100.00%, 86.13%); border: none;\">\n",
       "    <td style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\">\n",
       "        +1.611\n",
       "    </td>\n",
       "    <td style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">\n",
       "        x3588\n",
       "    </td>\n",
       "    \n",
       "</tr>\n",
       "        \n",
       "            <tr style=\"background-color: hsl(120, 100.00%, 86.87%); border: none;\">\n",
       "    <td style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\">\n",
       "        +1.488\n",
       "    </td>\n",
       "    <td style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">\n",
       "        x2497\n",
       "    </td>\n",
       "    \n",
       "</tr>\n",
       "        \n",
       "            <tr style=\"background-color: hsl(120, 100.00%, 87.71%); border: none;\">\n",
       "    <td style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\">\n",
       "        +1.354\n",
       "    </td>\n",
       "    <td style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">\n",
       "        x2621\n",
       "    </td>\n",
       "    \n",
       "</tr>\n",
       "        \n",
       "            <tr style=\"background-color: hsl(120, 100.00%, 87.97%); border: none;\">\n",
       "    <td style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\">\n",
       "        +1.315\n",
       "    </td>\n",
       "    <td style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">\n",
       "        x303\n",
       "    </td>\n",
       "    \n",
       "</tr>\n",
       "        \n",
       "            <tr style=\"background-color: hsl(120, 100.00%, 88.04%); border: none;\">\n",
       "    <td style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\">\n",
       "        +1.303\n",
       "    </td>\n",
       "    <td style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">\n",
       "        x4906\n",
       "    </td>\n",
       "    \n",
       "</tr>\n",
       "        \n",
       "            <tr style=\"background-color: hsl(120, 100.00%, 88.15%); border: none;\">\n",
       "    <td style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\">\n",
       "        +1.286\n",
       "    </td>\n",
       "    <td style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">\n",
       "        x1503\n",
       "    </td>\n",
       "    \n",
       "</tr>\n",
       "        \n",
       "            <tr style=\"background-color: hsl(120, 100.00%, 88.23%); border: none;\">\n",
       "    <td style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\">\n",
       "        +1.274\n",
       "    </td>\n",
       "    <td style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">\n",
       "        x1540\n",
       "    </td>\n",
       "    \n",
       "</tr>\n",
       "        \n",
       "            <tr style=\"background-color: hsl(120, 100.00%, 88.28%); border: none;\">\n",
       "    <td style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\">\n",
       "        +1.266\n",
       "    </td>\n",
       "    <td style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">\n",
       "        x4772\n",
       "    </td>\n",
       "    \n",
       "</tr>\n",
       "        \n",
       "            <tr style=\"background-color: hsl(120, 100.00%, 88.29%); border: none;\">\n",
       "    <td style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\">\n",
       "        +1.265\n",
       "    </td>\n",
       "    <td style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">\n",
       "        x3192\n",
       "    </td>\n",
       "    \n",
       "</tr>\n",
       "        \n",
       "            <tr style=\"background-color: hsl(120, 100.00%, 88.46%); border: none;\">\n",
       "    <td style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\">\n",
       "        +1.238\n",
       "    </td>\n",
       "    <td style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">\n",
       "        x513\n",
       "    </td>\n",
       "    \n",
       "</tr>\n",
       "        \n",
       "            <tr style=\"background-color: hsl(120, 100.00%, 88.66%); border: none;\">\n",
       "    <td style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\">\n",
       "        +1.208\n",
       "    </td>\n",
       "    <td style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">\n",
       "        x3299\n",
       "    </td>\n",
       "    \n",
       "</tr>\n",
       "        \n",
       "            <tr style=\"background-color: hsl(120, 100.00%, 88.83%); border: none;\">\n",
       "    <td style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\">\n",
       "        +1.182\n",
       "    </td>\n",
       "    <td style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">\n",
       "        x304\n",
       "    </td>\n",
       "    \n",
       "</tr>\n",
       "        \n",
       "            <tr style=\"background-color: hsl(120, 100.00%, 89.03%); border: none;\">\n",
       "    <td style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\">\n",
       "        +1.152\n",
       "    </td>\n",
       "    <td style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">\n",
       "        x1597\n",
       "    </td>\n",
       "    \n",
       "</tr>\n",
       "        \n",
       "        \n",
       "            <tr style=\"background-color: hsl(120, 100.00%, 89.03%); border: none;\">\n",
       "                <td colspan=\"2\" style=\"padding: 0 0.5em 0 0.5em; text-align: center; border: none; white-space: nowrap;\">\n",
       "                    <i>&hellip; 1254 more positive &hellip;</i>\n",
       "                </td>\n",
       "            </tr>\n",
       "        \n",
       "\n",
       "        \n",
       "            <tr style=\"background-color: hsl(0, 100.00%, 89.30%); border: none;\">\n",
       "                <td colspan=\"2\" style=\"padding: 0 0.5em 0 0.5em; text-align: center; border: none; white-space: nowrap;\">\n",
       "                    <i>&hellip; 3811 more negative &hellip;</i>\n",
       "                </td>\n",
       "            </tr>\n",
       "        \n",
       "        \n",
       "            <tr style=\"background-color: hsl(0, 100.00%, 89.30%); border: none;\">\n",
       "    <td style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\">\n",
       "        -1.111\n",
       "    </td>\n",
       "    <td style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">\n",
       "        x302\n",
       "    </td>\n",
       "    \n",
       "</tr>\n",
       "        \n",
       "\n",
       "        </tbody>\n",
       "    </table>\n",
       "\n",
       "                                \n",
       "                            \n",
       "                        </td>\n",
       "                    \n",
       "                \n",
       "            </tr>\n",
       "        </table>\n",
       "    \n",
       "\n",
       "    \n",
       "        \n",
       "\n",
       "\n",
       "    \n",
       "        \n",
       "\n",
       "\n",
       "    \n",
       "        \n",
       "\n",
       "\n",
       "    \n",
       "        \n",
       "\n",
       "\n",
       "    \n",
       "        \n",
       "\n",
       "\n",
       "    \n",
       "\n",
       "\n",
       "    \n",
       "\n",
       "    \n",
       "\n",
       "    \n",
       "\n",
       "\n",
       "    \n",
       "\n",
       "    \n",
       "\n",
       "    \n",
       "\n",
       "    \n",
       "\n",
       "    \n",
       "\n",
       "    \n",
       "\n",
       "\n",
       "    \n",
       "\n",
       "    \n",
       "\n",
       "    \n",
       "\n",
       "    \n",
       "\n",
       "    \n",
       "\n",
       "    \n",
       "\n",
       "\n",
       "\n"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Show the weights for each class/label\n",
    "eli5.show_weights(lr_model, top=20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "9b86de87-9681-498d-87f3-5a81754d1901",
   "metadata": {},
   "outputs": [],
   "source": [
    "class_names = y_labels.unique().tolist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "id": "2a262dcb-fc64-4a01-bf98-d9970274cc96",
   "metadata": {},
   "outputs": [],
   "source": [
    "feature_names = cv.get_feature_names_out()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "id": "b353d258-8938-44ce-bbc5-85c283980334",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "                \n",
       "                    <td style=\"padding: 0.5em; border: 1px solid black; text-align: center;\">\n",
       "                        <b>\n",
       "    \n",
       "        y=sadness\n",
       "    \n",
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       "\n",
       "top features\n",
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       "                \n",
       "                    <td style=\"padding: 0.5em; border: 1px solid black; text-align: center;\">\n",
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       "    \n",
       "        y=neutral\n",
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       "\n",
       "top features\n",
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       "                \n",
       "                    <td style=\"padding: 0.5em; border: 1px solid black; text-align: center;\">\n",
       "                        <b>\n",
       "    \n",
       "        y=anger\n",
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       "top features\n",
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       "                \n",
       "                    <td style=\"padding: 0.5em; border: 1px solid black; text-align: center;\">\n",
       "                        <b>\n",
       "    \n",
       "        y=fear\n",
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       "top features\n",
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       "                \n",
       "                    <td style=\"padding: 0.5em; border: 1px solid black; text-align: center;\">\n",
       "                        <b>\n",
       "    \n",
       "        y=joy\n",
       "    \n",
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       "\n",
       "top features\n",
       "                    </td>\n",
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       "                    \n",
       "                        <td style=\"padding: 0px; border: 1px solid black; vertical-align: top;\">\n",
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       "    \n",
       "    <table class=\"eli5-weights\"\n",
       "           style=\"border-collapse: collapse; border: none; margin-top: 0em; table-layout: auto; width: 100%;\">\n",
       "        <thead>\n",
       "        <tr style=\"border: none;\">\n",
       "            \n",
       "                <th style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\" title=\"Feature weights. Note that weights do not account for feature value scales, so if feature values have different scales, features with highest weights might not be the most important.\">\n",
       "                    Weight<sup>?</sup>\n",
       "                </th>\n",
       "            \n",
       "            <th style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">Feature</th>\n",
       "            \n",
       "        </tr>\n",
       "        </thead>\n",
       "        <tbody>\n",
       "        \n",
       "            <tr style=\"background-color: hsl(120, 100.00%, 81.67%); border: none;\">\n",
       "    <td style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\">\n",
       "        +2.397\n",
       "    </td>\n",
       "    <td style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">\n",
       "        angry\n",
       "    </td>\n",
       "    \n",
       "</tr>\n",
       "        \n",
       "            <tr style=\"background-color: hsl(120, 100.00%, 83.93%); border: none;\">\n",
       "    <td style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\">\n",
       "        +1.986\n",
       "    </td>\n",
       "    <td style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">\n",
       "        anger\n",
       "    </td>\n",
       "    \n",
       "</tr>\n",
       "        \n",
       "            <tr style=\"background-color: hsl(120, 100.00%, 84.39%); border: none;\">\n",
       "    <td style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\">\n",
       "        +1.906\n",
       "    </td>\n",
       "    <td style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">\n",
       "        furious\n",
       "    </td>\n",
       "    \n",
       "</tr>\n",
       "        \n",
       "            <tr style=\"background-color: hsl(120, 100.00%, 85.86%); border: none;\">\n",
       "    <td style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\">\n",
       "        +1.655\n",
       "    </td>\n",
       "    <td style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">\n",
       "        mad\n",
       "    </td>\n",
       "    \n",
       "</tr>\n",
       "        \n",
       "            <tr style=\"background-color: hsl(120, 100.00%, 86.02%); border: none;\">\n",
       "    <td style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\">\n",
       "        +1.629\n",
       "    </td>\n",
       "    <td style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">\n",
       "        indignant\n",
       "    </td>\n",
       "    \n",
       "</tr>\n",
       "        \n",
       "            <tr style=\"background-color: hsl(120, 100.00%, 86.36%); border: none;\">\n",
       "    <td style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\">\n",
       "        +1.572\n",
       "    </td>\n",
       "    <td style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">\n",
       "        exasperated\n",
       "    </td>\n",
       "    \n",
       "</tr>\n",
       "        \n",
       "            <tr style=\"background-color: hsl(120, 100.00%, 86.95%); border: none;\">\n",
       "    <td style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\">\n",
       "        +1.476\n",
       "    </td>\n",
       "    <td style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">\n",
       "        late\n",
       "    </td>\n",
       "    \n",
       "</tr>\n",
       "        \n",
       "            <tr style=\"background-color: hsl(120, 100.00%, 87.46%); border: none;\">\n",
       "    <td style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\">\n",
       "        +1.394\n",
       "    </td>\n",
       "    <td style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">\n",
       "        accused\n",
       "    </td>\n",
       "    \n",
       "</tr>\n",
       "        \n",
       "            <tr style=\"background-color: hsl(120, 100.00%, 87.83%); border: none;\">\n",
       "    <td style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\">\n",
       "        +1.335\n",
       "    </td>\n",
       "    <td style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">\n",
       "        warrant\n",
       "    </td>\n",
       "    \n",
       "</tr>\n",
       "        \n",
       "            <tr style=\"background-color: hsl(120, 100.00%, 87.86%); border: none;\">\n",
       "    <td style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\">\n",
       "        +1.332\n",
       "    </td>\n",
       "    <td style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">\n",
       "        resentful\n",
       "    </td>\n",
       "    \n",
       "</tr>\n",
       "        \n",
       "            <tr style=\"background-color: hsl(120, 100.00%, 88.60%); border: none;\">\n",
       "    <td style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\">\n",
       "        +1.216\n",
       "    </td>\n",
       "    <td style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">\n",
       "        roommate\n",
       "    </td>\n",
       "    \n",
       "</tr>\n",
       "        \n",
       "            <tr style=\"background-color: hsl(120, 100.00%, 88.62%); border: none;\">\n",
       "    <td style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\">\n",
       "        +1.214\n",
       "    </td>\n",
       "    <td style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">\n",
       "        miffed\n",
       "    </td>\n",
       "    \n",
       "</tr>\n",
       "        \n",
       "            <tr style=\"background-color: hsl(120, 100.00%, 88.67%); border: none;\">\n",
       "    <td style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\">\n",
       "        +1.205\n",
       "    </td>\n",
       "    <td style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">\n",
       "        hurry\n",
       "    </td>\n",
       "    \n",
       "</tr>\n",
       "        \n",
       "            <tr style=\"background-color: hsl(120, 100.00%, 89.14%); border: none;\">\n",
       "    <td style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\">\n",
       "        +1.135\n",
       "    </td>\n",
       "    <td style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">\n",
       "        quarrel\n",
       "    </td>\n",
       "    \n",
       "</tr>\n",
       "        \n",
       "            <tr style=\"background-color: hsl(120, 100.00%, 89.22%); border: none;\">\n",
       "    <td style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\">\n",
       "        +1.123\n",
       "    </td>\n",
       "    <td style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">\n",
       "        vexed\n",
       "    </td>\n",
       "    \n",
       "</tr>\n",
       "        \n",
       "            <tr style=\"background-color: hsl(120, 100.00%, 89.24%); border: none;\">\n",
       "    <td style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\">\n",
       "        +1.120\n",
       "    </td>\n",
       "    <td style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">\n",
       "        called\n",
       "    </td>\n",
       "    \n",
       "</tr>\n",
       "        \n",
       "            <tr style=\"background-color: hsl(120, 100.00%, 89.45%); border: none;\">\n",
       "    <td style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\">\n",
       "        +1.088\n",
       "    </td>\n",
       "    <td style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">\n",
       "        cross\n",
       "    </td>\n",
       "    \n",
       "</tr>\n",
       "        \n",
       "            <tr style=\"background-color: hsl(120, 100.00%, 89.56%); border: none;\">\n",
       "    <td style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\">\n",
       "        +1.073\n",
       "    </td>\n",
       "    <td style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">\n",
       "        exasperation\n",
       "    </td>\n",
       "    \n",
       "</tr>\n",
       "        \n",
       "            <tr style=\"background-color: hsl(120, 100.00%, 89.65%); border: none;\">\n",
       "    <td style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\">\n",
       "        +1.060\n",
       "    </td>\n",
       "    <td style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">\n",
       "        listen\n",
       "    </td>\n",
       "    \n",
       "</tr>\n",
       "        \n",
       "        \n",
       "            <tr style=\"background-color: hsl(120, 100.00%, 89.65%); border: none;\">\n",
       "                <td colspan=\"2\" style=\"padding: 0 0.5em 0 0.5em; text-align: center; border: none; white-space: nowrap;\">\n",
       "                    <i>&hellip; 1676 more positive &hellip;</i>\n",
       "                </td>\n",
       "            </tr>\n",
       "        \n",
       "\n",
       "        \n",
       "            <tr style=\"background-color: hsl(0, 100.00%, 89.44%); border: none;\">\n",
       "                <td colspan=\"2\" style=\"padding: 0 0.5em 0 0.5em; text-align: center; border: none; white-space: nowrap;\">\n",
       "                    <i>&hellip; 3389 more negative &hellip;</i>\n",
       "                </td>\n",
       "            </tr>\n",
       "        \n",
       "        \n",
       "            <tr style=\"background-color: hsl(0, 100.00%, 89.44%); border: none;\">\n",
       "    <td style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\">\n",
       "        -1.090\n",
       "    </td>\n",
       "    <td style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">\n",
       "        afraid\n",
       "    </td>\n",
       "    \n",
       "</tr>\n",
       "        \n",
       "\n",
       "        </tbody>\n",
       "    </table>\n",
       "\n",
       "                                \n",
       "                            \n",
       "                        </td>\n",
       "                    \n",
       "                        <td style=\"padding: 0px; border: 1px solid black; vertical-align: top;\">\n",
       "                            \n",
       "                                \n",
       "                                    \n",
       "                                    \n",
       "    \n",
       "    <table class=\"eli5-weights\"\n",
       "           style=\"border-collapse: collapse; border: none; margin-top: 0em; table-layout: auto; width: 100%;\">\n",
       "        <thead>\n",
       "        <tr style=\"border: none;\">\n",
       "            \n",
       "                <th style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\" title=\"Feature weights. Note that weights do not account for feature value scales, so if feature values have different scales, features with highest weights might not be the most important.\">\n",
       "                    Weight<sup>?</sup>\n",
       "                </th>\n",
       "            \n",
       "            <th style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">Feature</th>\n",
       "            \n",
       "        </tr>\n",
       "        </thead>\n",
       "        <tbody>\n",
       "        \n",
       "            <tr style=\"background-color: hsl(120, 100.00%, 82.01%); border: none;\">\n",
       "    <td style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\">\n",
       "        +2.335\n",
       "    </td>\n",
       "    <td style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">\n",
       "        horror\n",
       "    </td>\n",
       "    \n",
       "</tr>\n",
       "        \n",
       "            <tr style=\"background-color: hsl(120, 100.00%, 82.42%); border: none;\">\n",
       "    <td style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\">\n",
       "        +2.260\n",
       "    </td>\n",
       "    <td style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">\n",
       "        frightened\n",
       "    </td>\n",
       "    \n",
       "</tr>\n",
       "        \n",
       "            <tr style=\"background-color: hsl(120, 100.00%, 82.98%); border: none;\">\n",
       "    <td style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\">\n",
       "        +2.157\n",
       "    </td>\n",
       "    <td style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">\n",
       "        nervous\n",
       "    </td>\n",
       "    \n",
       "</tr>\n",
       "        \n",
       "            <tr style=\"background-color: hsl(120, 100.00%, 83.32%); border: none;\">\n",
       "    <td style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\">\n",
       "        +2.095\n",
       "    </td>\n",
       "    <td style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">\n",
       "        afraid\n",
       "    </td>\n",
       "    \n",
       "</tr>\n",
       "        \n",
       "            <tr style=\"background-color: hsl(120, 100.00%, 83.94%); border: none;\">\n",
       "    <td style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\">\n",
       "        +1.985\n",
       "    </td>\n",
       "    <td style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">\n",
       "        dismay\n",
       "    </td>\n",
       "    \n",
       "</tr>\n",
       "        \n",
       "            <tr style=\"background-color: hsl(120, 100.00%, 84.06%); border: none;\">\n",
       "    <td style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\">\n",
       "        +1.964\n",
       "    </td>\n",
       "    <td style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">\n",
       "        scared\n",
       "    </td>\n",
       "    \n",
       "</tr>\n",
       "        \n",
       "            <tr style=\"background-color: hsl(120, 100.00%, 84.26%); border: none;\">\n",
       "    <td style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\">\n",
       "        +1.929\n",
       "    </td>\n",
       "    <td style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">\n",
       "        fear\n",
       "    </td>\n",
       "    \n",
       "</tr>\n",
       "        \n",
       "            <tr style=\"background-color: hsl(120, 100.00%, 84.86%); border: none;\">\n",
       "    <td style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\">\n",
       "        +1.824\n",
       "    </td>\n",
       "    <td style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">\n",
       "        anxious\n",
       "    </td>\n",
       "    \n",
       "</tr>\n",
       "        \n",
       "            <tr style=\"background-color: hsl(120, 100.00%, 86.44%); border: none;\">\n",
       "    <td style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\">\n",
       "        +1.559\n",
       "    </td>\n",
       "    <td style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">\n",
       "        night\n",
       "    </td>\n",
       "    \n",
       "</tr>\n",
       "        \n",
       "            <tr style=\"background-color: hsl(120, 100.00%, 87.25%); border: none;\">\n",
       "    <td style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\">\n",
       "        +1.428\n",
       "    </td>\n",
       "    <td style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">\n",
       "        concerned\n",
       "    </td>\n",
       "    \n",
       "</tr>\n",
       "        \n",
       "            <tr style=\"background-color: hsl(120, 100.00%, 87.63%); border: none;\">\n",
       "    <td style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\">\n",
       "        +1.367\n",
       "    </td>\n",
       "    <td style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">\n",
       "        dark\n",
       "    </td>\n",
       "    \n",
       "</tr>\n",
       "        \n",
       "            <tr style=\"background-color: hsl(120, 100.00%, 88.05%); border: none;\">\n",
       "    <td style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\">\n",
       "        +1.302\n",
       "    </td>\n",
       "    <td style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">\n",
       "        fight\n",
       "    </td>\n",
       "    \n",
       "</tr>\n",
       "        \n",
       "            <tr style=\"background-color: hsl(120, 100.00%, 88.24%); border: none;\">\n",
       "    <td style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\">\n",
       "        +1.272\n",
       "    </td>\n",
       "    <td style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">\n",
       "        disquiet\n",
       "    </td>\n",
       "    \n",
       "</tr>\n",
       "        \n",
       "            <tr style=\"background-color: hsl(120, 100.00%, 89.22%); border: none;\">\n",
       "    <td style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\">\n",
       "        +1.123\n",
       "    </td>\n",
       "    <td style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">\n",
       "        earthquake\n",
       "    </td>\n",
       "    \n",
       "</tr>\n",
       "        \n",
       "            <tr style=\"background-color: hsl(120, 100.00%, 89.27%); border: none;\">\n",
       "    <td style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\">\n",
       "        +1.117\n",
       "    </td>\n",
       "    <td style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">\n",
       "        threatened\n",
       "    </td>\n",
       "    \n",
       "</tr>\n",
       "        \n",
       "            <tr style=\"background-color: hsl(120, 100.00%, 89.30%); border: none;\">\n",
       "    <td style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\">\n",
       "        +1.111\n",
       "    </td>\n",
       "    <td style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">\n",
       "        single\n",
       "    </td>\n",
       "    \n",
       "</tr>\n",
       "        \n",
       "            <tr style=\"background-color: hsl(120, 100.00%, 89.55%); border: none;\">\n",
       "    <td style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\">\n",
       "        +1.074\n",
       "    </td>\n",
       "    <td style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">\n",
       "        factor\n",
       "    </td>\n",
       "    \n",
       "</tr>\n",
       "        \n",
       "            <tr style=\"background-color: hsl(120, 100.00%, 89.62%); border: none;\">\n",
       "    <td style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\">\n",
       "        +1.064\n",
       "    </td>\n",
       "    <td style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">\n",
       "        exam\n",
       "    </td>\n",
       "    \n",
       "</tr>\n",
       "        \n",
       "            <tr style=\"background-color: hsl(120, 100.00%, 90.14%); border: none;\">\n",
       "    <td style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\">\n",
       "        +0.988\n",
       "    </td>\n",
       "    <td style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">\n",
       "        alarmed\n",
       "    </td>\n",
       "    \n",
       "</tr>\n",
       "        \n",
       "            <tr style=\"background-color: hsl(120, 100.00%, 90.22%); border: none;\">\n",
       "    <td style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\">\n",
       "        +0.977\n",
       "    </td>\n",
       "    <td style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">\n",
       "        horrified\n",
       "    </td>\n",
       "    \n",
       "</tr>\n",
       "        \n",
       "        \n",
       "            <tr style=\"background-color: hsl(120, 100.00%, 90.22%); border: none;\">\n",
       "                <td colspan=\"2\" style=\"padding: 0 0.5em 0 0.5em; text-align: center; border: none; white-space: nowrap;\">\n",
       "                    <i>&hellip; 1828 more positive &hellip;</i>\n",
       "                </td>\n",
       "            </tr>\n",
       "        \n",
       "\n",
       "        \n",
       "            <tr style=\"background-color: hsl(0, 100.00%, 80.00%); border: none;\">\n",
       "                <td colspan=\"2\" style=\"padding: 0 0.5em 0 0.5em; text-align: center; border: none; white-space: nowrap;\">\n",
       "                    <i>&hellip; 3237 more negative &hellip;</i>\n",
       "                </td>\n",
       "            </tr>\n",
       "        \n",
       "        \n",
       "\n",
       "        </tbody>\n",
       "    </table>\n",
       "\n",
       "                                \n",
       "                            \n",
       "                        </td>\n",
       "                    \n",
       "                        <td style=\"padding: 0px; border: 1px solid black; vertical-align: top;\">\n",
       "                            \n",
       "                                \n",
       "                                    \n",
       "                                    \n",
       "    \n",
       "    <table class=\"eli5-weights\"\n",
       "           style=\"border-collapse: collapse; border: none; margin-top: 0em; table-layout: auto; width: 100%;\">\n",
       "        <thead>\n",
       "        <tr style=\"border: none;\">\n",
       "            \n",
       "                <th style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\" title=\"Feature weights. Note that weights do not account for feature value scales, so if feature values have different scales, features with highest weights might not be the most important.\">\n",
       "                    Weight<sup>?</sup>\n",
       "                </th>\n",
       "            \n",
       "            <th style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">Feature</th>\n",
       "            \n",
       "        </tr>\n",
       "        </thead>\n",
       "        <tbody>\n",
       "        \n",
       "            <tr style=\"background-color: hsl(120, 100.00%, 83.28%); border: none;\">\n",
       "    <td style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\">\n",
       "        +2.103\n",
       "    </td>\n",
       "    <td style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">\n",
       "        happy\n",
       "    </td>\n",
       "    \n",
       "</tr>\n",
       "        \n",
       "            <tr style=\"background-color: hsl(120, 100.00%, 84.62%); border: none;\">\n",
       "    <td style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\">\n",
       "        +1.867\n",
       "    </td>\n",
       "    <td style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">\n",
       "        joy\n",
       "    </td>\n",
       "    \n",
       "</tr>\n",
       "        \n",
       "            <tr style=\"background-color: hsl(120, 100.00%, 84.67%); border: none;\">\n",
       "    <td style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\">\n",
       "        +1.857\n",
       "    </td>\n",
       "    <td style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">\n",
       "        excitement\n",
       "    </td>\n",
       "    \n",
       "</tr>\n",
       "        \n",
       "            <tr style=\"background-color: hsl(120, 100.00%, 85.14%); border: none;\">\n",
       "    <td style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\">\n",
       "        +1.777\n",
       "    </td>\n",
       "    <td style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">\n",
       "        excited\n",
       "    </td>\n",
       "    \n",
       "</tr>\n",
       "        \n",
       "            <tr style=\"background-color: hsl(120, 100.00%, 85.38%); border: none;\">\n",
       "    <td style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\">\n",
       "        +1.736\n",
       "    </td>\n",
       "    <td style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">\n",
       "        passed\n",
       "    </td>\n",
       "    \n",
       "</tr>\n",
       "        \n",
       "            <tr style=\"background-color: hsl(120, 100.00%, 86.26%); border: none;\">\n",
       "    <td style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\">\n",
       "        +1.588\n",
       "    </td>\n",
       "    <td style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">\n",
       "        ecstatic\n",
       "    </td>\n",
       "    \n",
       "</tr>\n",
       "        \n",
       "            <tr style=\"background-color: hsl(120, 100.00%, 86.43%); border: none;\">\n",
       "    <td style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\">\n",
       "        +1.561\n",
       "    </td>\n",
       "    <td style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">\n",
       "        delighted\n",
       "    </td>\n",
       "    \n",
       "</tr>\n",
       "        \n",
       "            <tr style=\"background-color: hsl(120, 100.00%, 86.55%); border: none;\">\n",
       "    <td style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\">\n",
       "        +1.541\n",
       "    </td>\n",
       "    <td style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">\n",
       "        pleased\n",
       "    </td>\n",
       "    \n",
       "</tr>\n",
       "        \n",
       "            <tr style=\"background-color: hsl(120, 100.00%, 86.79%); border: none;\">\n",
       "    <td style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\">\n",
       "        +1.502\n",
       "    </td>\n",
       "    <td style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">\n",
       "        born\n",
       "    </td>\n",
       "    \n",
       "</tr>\n",
       "        \n",
       "            <tr style=\"background-color: hsl(120, 100.00%, 87.05%); border: none;\">\n",
       "    <td style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\">\n",
       "        +1.459\n",
       "    </td>\n",
       "    <td style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">\n",
       "        delight\n",
       "    </td>\n",
       "    \n",
       "</tr>\n",
       "        \n",
       "            <tr style=\"background-color: hsl(120, 100.00%, 87.74%); border: none;\">\n",
       "    <td style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\">\n",
       "        +1.351\n",
       "    </td>\n",
       "    <td style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">\n",
       "        returned\n",
       "    </td>\n",
       "    \n",
       "</tr>\n",
       "        \n",
       "            <tr style=\"background-color: hsl(120, 100.00%, 87.96%); border: none;\">\n",
       "    <td style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\">\n",
       "        +1.315\n",
       "    </td>\n",
       "    <td style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">\n",
       "        great\n",
       "    </td>\n",
       "    \n",
       "</tr>\n",
       "        \n",
       "            <tr style=\"background-color: hsl(120, 100.00%, 88.09%); border: none;\">\n",
       "    <td style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\">\n",
       "        +1.294\n",
       "    </td>\n",
       "    <td style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">\n",
       "        amusement\n",
       "    </td>\n",
       "    \n",
       "</tr>\n",
       "        \n",
       "            <tr style=\"background-color: hsl(120, 100.00%, 88.18%); border: none;\">\n",
       "    <td style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\">\n",
       "        +1.281\n",
       "    </td>\n",
       "    <td style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">\n",
       "        nice\n",
       "    </td>\n",
       "    \n",
       "</tr>\n",
       "        \n",
       "            <tr style=\"background-color: hsl(120, 100.00%, 88.49%); border: none;\">\n",
       "    <td style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\">\n",
       "        +1.233\n",
       "    </td>\n",
       "    <td style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">\n",
       "        thank\n",
       "    </td>\n",
       "    \n",
       "</tr>\n",
       "        \n",
       "            <tr style=\"background-color: hsl(120, 100.00%, 88.89%); border: none;\">\n",
       "    <td style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\">\n",
       "        +1.173\n",
       "    </td>\n",
       "    <td style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">\n",
       "        lets\n",
       "    </td>\n",
       "    \n",
       "</tr>\n",
       "        \n",
       "            <tr style=\"background-color: hsl(120, 100.00%, 89.08%); border: none;\">\n",
       "    <td style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\">\n",
       "        +1.144\n",
       "    </td>\n",
       "    <td style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">\n",
       "        chosen\n",
       "    </td>\n",
       "    \n",
       "</tr>\n",
       "        \n",
       "            <tr style=\"background-color: hsl(120, 100.00%, 89.17%); border: none;\">\n",
       "    <td style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\">\n",
       "        +1.131\n",
       "    </td>\n",
       "    <td style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">\n",
       "        overjoyed\n",
       "    </td>\n",
       "    \n",
       "</tr>\n",
       "        \n",
       "            <tr style=\"background-color: hsl(120, 100.00%, 89.20%); border: none;\">\n",
       "    <td style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\">\n",
       "        +1.126\n",
       "    </td>\n",
       "    <td style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">\n",
       "        period\n",
       "    </td>\n",
       "    \n",
       "</tr>\n",
       "        \n",
       "            <tr style=\"background-color: hsl(120, 100.00%, 89.20%); border: none;\">\n",
       "    <td style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\">\n",
       "        +1.126\n",
       "    </td>\n",
       "    <td style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">\n",
       "        accepted\n",
       "    </td>\n",
       "    \n",
       "</tr>\n",
       "        \n",
       "        \n",
       "            <tr style=\"background-color: hsl(120, 100.00%, 89.20%); border: none;\">\n",
       "                <td colspan=\"2\" style=\"padding: 0 0.5em 0 0.5em; text-align: center; border: none; white-space: nowrap;\">\n",
       "                    <i>&hellip; 1287 more positive &hellip;</i>\n",
       "                </td>\n",
       "            </tr>\n",
       "        \n",
       "\n",
       "        \n",
       "            <tr style=\"background-color: hsl(0, 100.00%, 80.00%); border: none;\">\n",
       "                <td colspan=\"2\" style=\"padding: 0 0.5em 0 0.5em; text-align: center; border: none; white-space: nowrap;\">\n",
       "                    <i>&hellip; 3778 more negative &hellip;</i>\n",
       "                </td>\n",
       "            </tr>\n",
       "        \n",
       "        \n",
       "\n",
       "        </tbody>\n",
       "    </table>\n",
       "\n",
       "                                \n",
       "                            \n",
       "                        </td>\n",
       "                    \n",
       "                        <td style=\"padding: 0px; border: 1px solid black; vertical-align: top;\">\n",
       "                            \n",
       "                                \n",
       "                                    \n",
       "                                    \n",
       "    \n",
       "    <table class=\"eli5-weights\"\n",
       "           style=\"border-collapse: collapse; border: none; margin-top: 0em; table-layout: auto; width: 100%;\">\n",
       "        <thead>\n",
       "        <tr style=\"border: none;\">\n",
       "            \n",
       "                <th style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\" title=\"Feature weights. Note that weights do not account for feature value scales, so if feature values have different scales, features with highest weights might not be the most important.\">\n",
       "                    Weight<sup>?</sup>\n",
       "                </th>\n",
       "            \n",
       "            <th style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">Feature</th>\n",
       "            \n",
       "        </tr>\n",
       "        </thead>\n",
       "        <tbody>\n",
       "        \n",
       "            <tr style=\"background-color: hsl(120, 100.00%, 87.82%); border: none;\">\n",
       "    <td style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\">\n",
       "        +1.337\n",
       "    </td>\n",
       "    <td style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">\n",
       "        &lt;BIAS&gt;\n",
       "    </td>\n",
       "    \n",
       "</tr>\n",
       "        \n",
       "            <tr style=\"background-color: hsl(120, 100.00%, 89.66%); border: none;\">\n",
       "    <td style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\">\n",
       "        +1.058\n",
       "    </td>\n",
       "    <td style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">\n",
       "        hi\n",
       "    </td>\n",
       "    \n",
       "</tr>\n",
       "        \n",
       "            <tr style=\"background-color: hsl(120, 100.00%, 90.11%); border: none;\">\n",
       "    <td style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\">\n",
       "        +0.993\n",
       "    </td>\n",
       "    <td style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">\n",
       "        probably\n",
       "    </td>\n",
       "    \n",
       "</tr>\n",
       "        \n",
       "            <tr style=\"background-color: hsl(120, 100.00%, 90.32%); border: none;\">\n",
       "    <td style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\">\n",
       "        +0.963\n",
       "    </td>\n",
       "    <td style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">\n",
       "        yes\n",
       "    </td>\n",
       "    \n",
       "</tr>\n",
       "        \n",
       "        \n",
       "            <tr style=\"background-color: hsl(120, 100.00%, 90.32%); border: none;\">\n",
       "                <td colspan=\"2\" style=\"padding: 0 0.5em 0 0.5em; text-align: center; border: none; white-space: nowrap;\">\n",
       "                    <i>&hellip; 532 more positive &hellip;</i>\n",
       "                </td>\n",
       "            </tr>\n",
       "        \n",
       "\n",
       "        \n",
       "            <tr style=\"background-color: hsl(0, 100.00%, 90.47%); border: none;\">\n",
       "                <td colspan=\"2\" style=\"padding: 0 0.5em 0 0.5em; text-align: center; border: none; white-space: nowrap;\">\n",
       "                    <i>&hellip; 4533 more negative &hellip;</i>\n",
       "                </td>\n",
       "            </tr>\n",
       "        \n",
       "        \n",
       "            <tr style=\"background-color: hsl(0, 100.00%, 90.47%); border: none;\">\n",
       "    <td style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\">\n",
       "        -0.942\n",
       "    </td>\n",
       "    <td style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">\n",
       "        friends\n",
       "    </td>\n",
       "    \n",
       "</tr>\n",
       "        \n",
       "            <tr style=\"background-color: hsl(0, 100.00%, 90.39%); border: none;\">\n",
       "    <td style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\">\n",
       "        -0.954\n",
       "    </td>\n",
       "    <td style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">\n",
       "        terrible\n",
       "    </td>\n",
       "    \n",
       "</tr>\n",
       "        \n",
       "            <tr style=\"background-color: hsl(0, 100.00%, 90.37%); border: none;\">\n",
       "    <td style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\">\n",
       "        -0.957\n",
       "    </td>\n",
       "    <td style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">\n",
       "        nervous\n",
       "    </td>\n",
       "    \n",
       "</tr>\n",
       "        \n",
       "            <tr style=\"background-color: hsl(0, 100.00%, 90.28%); border: none;\">\n",
       "    <td style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\">\n",
       "        -0.970\n",
       "    </td>\n",
       "    <td style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">\n",
       "        boyfriend\n",
       "    </td>\n",
       "    \n",
       "</tr>\n",
       "        \n",
       "            <tr style=\"background-color: hsl(0, 100.00%, 90.27%); border: none;\">\n",
       "    <td style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\">\n",
       "        -0.971\n",
       "    </td>\n",
       "    <td style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">\n",
       "        told\n",
       "    </td>\n",
       "    \n",
       "</tr>\n",
       "        \n",
       "            <tr style=\"background-color: hsl(0, 100.00%, 89.78%); border: none;\">\n",
       "    <td style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\">\n",
       "        -1.041\n",
       "    </td>\n",
       "    <td style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">\n",
       "        girlfriend\n",
       "    </td>\n",
       "    \n",
       "</tr>\n",
       "        \n",
       "            <tr style=\"background-color: hsl(0, 100.00%, 89.57%); border: none;\">\n",
       "    <td style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\">\n",
       "        -1.072\n",
       "    </td>\n",
       "    <td style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">\n",
       "        job\n",
       "    </td>\n",
       "    \n",
       "</tr>\n",
       "        \n",
       "            <tr style=\"background-color: hsl(0, 100.00%, 89.32%); border: none;\">\n",
       "    <td style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\">\n",
       "        -1.108\n",
       "    </td>\n",
       "    <td style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">\n",
       "        face\n",
       "    </td>\n",
       "    \n",
       "</tr>\n",
       "        \n",
       "            <tr style=\"background-color: hsl(0, 100.00%, 89.32%); border: none;\">\n",
       "    <td style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\">\n",
       "        -1.108\n",
       "    </td>\n",
       "    <td style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">\n",
       "        saw\n",
       "    </td>\n",
       "    \n",
       "</tr>\n",
       "        \n",
       "            <tr style=\"background-color: hsl(0, 100.00%, 89.14%); border: none;\">\n",
       "    <td style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\">\n",
       "        -1.136\n",
       "    </td>\n",
       "    <td style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">\n",
       "        school\n",
       "    </td>\n",
       "    \n",
       "</tr>\n",
       "        \n",
       "            <tr style=\"background-color: hsl(0, 100.00%, 89.09%); border: none;\">\n",
       "    <td style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\">\n",
       "        -1.143\n",
       "    </td>\n",
       "    <td style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">\n",
       "        car\n",
       "    </td>\n",
       "    \n",
       "</tr>\n",
       "        \n",
       "            <tr style=\"background-color: hsl(0, 100.00%, 88.89%); border: none;\">\n",
       "    <td style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\">\n",
       "        -1.173\n",
       "    </td>\n",
       "    <td style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">\n",
       "        feel\n",
       "    </td>\n",
       "    \n",
       "</tr>\n",
       "        \n",
       "            <tr style=\"background-color: hsl(0, 100.00%, 88.84%); border: none;\">\n",
       "    <td style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\">\n",
       "        -1.180\n",
       "    </td>\n",
       "    <td style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">\n",
       "        me\n",
       "    </td>\n",
       "    \n",
       "</tr>\n",
       "        \n",
       "            <tr style=\"background-color: hsl(0, 100.00%, 88.60%); border: none;\">\n",
       "    <td style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\">\n",
       "        -1.216\n",
       "    </td>\n",
       "    <td style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">\n",
       "        felt\n",
       "    </td>\n",
       "    \n",
       "</tr>\n",
       "        \n",
       "            <tr style=\"background-color: hsl(0, 100.00%, 88.40%); border: none;\">\n",
       "    <td style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\">\n",
       "        -1.247\n",
       "    </td>\n",
       "    <td style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">\n",
       "        night\n",
       "    </td>\n",
       "    \n",
       "</tr>\n",
       "        \n",
       "            <tr style=\"background-color: hsl(0, 100.00%, 85.71%); border: none;\">\n",
       "    <td style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\">\n",
       "        -1.680\n",
       "    </td>\n",
       "    <td style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">\n",
       "        friend\n",
       "    </td>\n",
       "    \n",
       "</tr>\n",
       "        \n",
       "\n",
       "        </tbody>\n",
       "    </table>\n",
       "\n",
       "                                \n",
       "                            \n",
       "                        </td>\n",
       "                    \n",
       "                        <td style=\"padding: 0px; border: 1px solid black; vertical-align: top;\">\n",
       "                            \n",
       "                                \n",
       "                                    \n",
       "                                    \n",
       "    \n",
       "    <table class=\"eli5-weights\"\n",
       "           style=\"border-collapse: collapse; border: none; margin-top: 0em; table-layout: auto; width: 100%;\">\n",
       "        <thead>\n",
       "        <tr style=\"border: none;\">\n",
       "            \n",
       "                <th style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\" title=\"Feature weights. Note that weights do not account for feature value scales, so if feature values have different scales, features with highest weights might not be the most important.\">\n",
       "                    Weight<sup>?</sup>\n",
       "                </th>\n",
       "            \n",
       "            <th style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">Feature</th>\n",
       "            \n",
       "        </tr>\n",
       "        </thead>\n",
       "        <tbody>\n",
       "        \n",
       "            <tr style=\"background-color: hsl(120, 100.00%, 80.00%); border: none;\">\n",
       "    <td style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\">\n",
       "        +2.716\n",
       "    </td>\n",
       "    <td style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">\n",
       "        sorry\n",
       "    </td>\n",
       "    \n",
       "</tr>\n",
       "        \n",
       "            <tr style=\"background-color: hsl(120, 100.00%, 81.45%); border: none;\">\n",
       "    <td style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\">\n",
       "        +2.439\n",
       "    </td>\n",
       "    <td style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">\n",
       "        died\n",
       "    </td>\n",
       "    \n",
       "</tr>\n",
       "        \n",
       "            <tr style=\"background-color: hsl(120, 100.00%, 81.50%); border: none;\">\n",
       "    <td style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\">\n",
       "        +2.429\n",
       "    </td>\n",
       "    <td style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">\n",
       "        sad\n",
       "    </td>\n",
       "    \n",
       "</tr>\n",
       "        \n",
       "            <tr style=\"background-color: hsl(120, 100.00%, 83.76%); border: none;\">\n",
       "    <td style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\">\n",
       "        +2.016\n",
       "    </td>\n",
       "    <td style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">\n",
       "        miserable\n",
       "    </td>\n",
       "    \n",
       "</tr>\n",
       "        \n",
       "            <tr style=\"background-color: hsl(120, 100.00%, 85.90%); border: none;\">\n",
       "    <td style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\">\n",
       "        +1.648\n",
       "    </td>\n",
       "    <td style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">\n",
       "        despondent\n",
       "    </td>\n",
       "    \n",
       "</tr>\n",
       "        \n",
       "            <tr style=\"background-color: hsl(120, 100.00%, 86.00%); border: none;\">\n",
       "    <td style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\">\n",
       "        +1.631\n",
       "    </td>\n",
       "    <td style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">\n",
       "        death\n",
       "    </td>\n",
       "    \n",
       "</tr>\n",
       "        \n",
       "            <tr style=\"background-color: hsl(120, 100.00%, 86.13%); border: none;\">\n",
       "    <td style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\">\n",
       "        +1.611\n",
       "    </td>\n",
       "    <td style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">\n",
       "        mood\n",
       "    </td>\n",
       "    \n",
       "</tr>\n",
       "        \n",
       "            <tr style=\"background-color: hsl(120, 100.00%, 86.87%); border: none;\">\n",
       "    <td style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\">\n",
       "        +1.488\n",
       "    </td>\n",
       "    <td style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">\n",
       "        grief\n",
       "    </td>\n",
       "    \n",
       "</tr>\n",
       "        \n",
       "            <tr style=\"background-color: hsl(120, 100.00%, 87.71%); border: none;\">\n",
       "    <td style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\">\n",
       "        +1.354\n",
       "    </td>\n",
       "    <td style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">\n",
       "        heartbroken\n",
       "    </td>\n",
       "    \n",
       "</tr>\n",
       "        \n",
       "            <tr style=\"background-color: hsl(120, 100.00%, 87.97%); border: none;\">\n",
       "    <td style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\">\n",
       "        +1.315\n",
       "    </td>\n",
       "    <td style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">\n",
       "        anguish\n",
       "    </td>\n",
       "    \n",
       "</tr>\n",
       "        \n",
       "            <tr style=\"background-color: hsl(120, 100.00%, 88.04%); border: none;\">\n",
       "    <td style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\">\n",
       "        +1.303\n",
       "    </td>\n",
       "    <td style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">\n",
       "        separated\n",
       "    </td>\n",
       "    \n",
       "</tr>\n",
       "        \n",
       "            <tr style=\"background-color: hsl(120, 100.00%, 88.15%); border: none;\">\n",
       "    <td style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\">\n",
       "        +1.286\n",
       "    </td>\n",
       "    <td style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">\n",
       "        depressed\n",
       "    </td>\n",
       "    \n",
       "</tr>\n",
       "        \n",
       "            <tr style=\"background-color: hsl(120, 100.00%, 88.23%); border: none;\">\n",
       "    <td style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\">\n",
       "        +1.274\n",
       "    </td>\n",
       "    <td style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">\n",
       "        devastated\n",
       "    </td>\n",
       "    \n",
       "</tr>\n",
       "        \n",
       "            <tr style=\"background-color: hsl(120, 100.00%, 88.28%); border: none;\">\n",
       "    <td style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\">\n",
       "        +1.266\n",
       "    </td>\n",
       "    <td style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">\n",
       "        sadness\n",
       "    </td>\n",
       "    \n",
       "</tr>\n",
       "        \n",
       "            <tr style=\"background-color: hsl(120, 100.00%, 88.29%); border: none;\">\n",
       "    <td style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\">\n",
       "        +1.265\n",
       "    </td>\n",
       "    <td style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">\n",
       "        left\n",
       "    </td>\n",
       "    \n",
       "</tr>\n",
       "        \n",
       "            <tr style=\"background-color: hsl(120, 100.00%, 88.46%); border: none;\">\n",
       "    <td style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\">\n",
       "        +1.238\n",
       "    </td>\n",
       "    <td style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">\n",
       "        bag\n",
       "    </td>\n",
       "    \n",
       "</tr>\n",
       "        \n",
       "            <tr style=\"background-color: hsl(120, 100.00%, 88.66%); border: none;\">\n",
       "    <td style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\">\n",
       "        +1.208\n",
       "    </td>\n",
       "    <td style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">\n",
       "        lost\n",
       "    </td>\n",
       "    \n",
       "</tr>\n",
       "        \n",
       "            <tr style=\"background-color: hsl(120, 100.00%, 88.83%); border: none;\">\n",
       "    <td style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\">\n",
       "        +1.182\n",
       "    </td>\n",
       "    <td style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">\n",
       "        anguished\n",
       "    </td>\n",
       "    \n",
       "</tr>\n",
       "        \n",
       "            <tr style=\"background-color: hsl(120, 100.00%, 89.03%); border: none;\">\n",
       "    <td style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\">\n",
       "        +1.152\n",
       "    </td>\n",
       "    <td style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">\n",
       "        disconsolate\n",
       "    </td>\n",
       "    \n",
       "</tr>\n",
       "        \n",
       "        \n",
       "            <tr style=\"background-color: hsl(120, 100.00%, 89.03%); border: none;\">\n",
       "                <td colspan=\"2\" style=\"padding: 0 0.5em 0 0.5em; text-align: center; border: none; white-space: nowrap;\">\n",
       "                    <i>&hellip; 1254 more positive &hellip;</i>\n",
       "                </td>\n",
       "            </tr>\n",
       "        \n",
       "\n",
       "        \n",
       "            <tr style=\"background-color: hsl(0, 100.00%, 89.30%); border: none;\">\n",
       "                <td colspan=\"2\" style=\"padding: 0 0.5em 0 0.5em; text-align: center; border: none; white-space: nowrap;\">\n",
       "                    <i>&hellip; 3811 more negative &hellip;</i>\n",
       "                </td>\n",
       "            </tr>\n",
       "        \n",
       "        \n",
       "            <tr style=\"background-color: hsl(0, 100.00%, 89.30%); border: none;\">\n",
       "    <td style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\">\n",
       "        -1.111\n",
       "    </td>\n",
       "    <td style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">\n",
       "        angry\n",
       "    </td>\n",
       "    \n",
       "</tr>\n",
       "        \n",
       "\n",
       "        </tbody>\n",
       "    </table>\n",
       "\n",
       "                                \n",
       "                            \n",
       "                        </td>\n",
       "                    \n",
       "                \n",
       "            </tr>\n",
       "        </table>\n",
       "    \n",
       "\n",
       "    \n",
       "        \n",
       "\n",
       "\n",
       "    \n",
       "        \n",
       "\n",
       "\n",
       "    \n",
       "        \n",
       "\n",
       "\n",
       "    \n",
       "        \n",
       "\n",
       "\n",
       "    \n",
       "        \n",
       "\n",
       "\n",
       "    \n",
       "\n",
       "\n",
       "    \n",
       "\n",
       "    \n",
       "\n",
       "    \n",
       "\n",
       "\n",
       "    \n",
       "\n",
       "    \n",
       "\n",
       "    \n",
       "\n",
       "    \n",
       "\n",
       "    \n",
       "\n",
       "    \n",
       "\n",
       "\n",
       "    \n",
       "\n",
       "    \n",
       "\n",
       "    \n",
       "\n",
       "    \n",
       "\n",
       "    \n",
       "\n",
       "    \n",
       "\n",
       "\n",
       "\n"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "eli5.show_weights(lr_model, feature_names=feature_names, target_names=class_names)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "id": "70796357-9a87-4afd-9fd9-b2f7ffaa0a02",
   "metadata": {
    "collapsed": true,
    "jupyter": {
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    {
     "data": {
      "text/plain": [
       "\u001b[1;31mSignature:\u001b[0m \u001b[0meli5\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mshow_weights\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mestimator\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
       "\u001b[1;31mDocstring:\u001b[0m\n",
       "Return an explanation of estimator parameters (weights)\n",
       "as an IPython.display.HTML object. Use this function\n",
       "to show classifier weights in IPython.\n",
       "\n",
       ":func:`show_weights` accepts all\n",
       ":func:`eli5.explain_weights` arguments and all\n",
       ":func:`eli5.formatters.html.format_as_html`\n",
       "keyword arguments, so it is possible to get explanation and\n",
       "customize formatting in a single call.\n",
       "\n",
       "Parameters\n",
       "----------\n",
       "estimator : object\n",
       "    Estimator instance. This argument must be positional.\n",
       "\n",
       "top : int or (int, int) tuple, optional\n",
       "    Number of features to show. When ``top`` is int, ``top`` features with\n",
       "    a highest absolute values are shown. When it is (pos, neg) tuple,\n",
       "    no more than ``pos`` positive features and no more than ``neg``\n",
       "    negative features is shown. ``None`` value means no limit.\n",
       "\n",
       "    This argument may be supported or not, depending on estimator type.\n",
       "\n",
       "target_names : list[str] or {'old_name': 'new_name'} dict, optional\n",
       "    Names of targets or classes. This argument can be used to provide\n",
       "    human-readable class/target names for estimators which don't expose\n",
       "    clss names themselves. It can be also used to rename estimator-provided\n",
       "    classes before displaying them.\n",
       "\n",
       "    This argument may be supported or not, depending on estimator type.\n",
       "\n",
       "targets : list, optional\n",
       "    Order of class/target names to show. This argument can be also used\n",
       "    to show information only for a subset of classes. It should be a list\n",
       "    of class / target names which match either names provided by\n",
       "    an estimator or names defined in ``target_names`` parameter.\n",
       "\n",
       "    This argument may be supported or not, depending on estimator type.\n",
       "\n",
       "feature_names : list, optional\n",
       "    A list of feature names. It allows to specify feature\n",
       "    names when they are not provided by an estimator object.\n",
       "\n",
       "    This argument may be supported or not, depending on estimator type.\n",
       "\n",
       "feature_re : str, optional\n",
       "    Only feature names which match ``feature_re`` regex are shown\n",
       "    (more precisely, ``re.search(feature_re, x)`` is checked).\n",
       "\n",
       "feature_filter : Callable[[str], bool], optional\n",
       "    Only feature names for which ``feature_filter`` function returns True\n",
       "    are shown.\n",
       "\n",
       "show : List[str], optional\n",
       "    List of sections to show. Allowed values:\n",
       "\n",
       "    * 'targets' - per-target feature weights;\n",
       "    * 'transition_features' - transition features of a CRF model;\n",
       "    * 'feature_importances' - feature importances of a decision tree or\n",
       "      an ensemble-based estimator;\n",
       "    * 'decision_tree' - decision tree in a graphical form;\n",
       "    * 'method' - a string with explanation method;\n",
       "    * 'description' - description of explanation method and its caveats.\n",
       "\n",
       "    ``eli5.formatters.fields`` provides constants that cover common cases:\n",
       "    ``INFO`` (method and description), ``WEIGHTS`` (all the rest),\n",
       "    and ``ALL`` (all).\n",
       "\n",
       "horizontal_layout : bool\n",
       "    When True, feature weight tables are printed horizontally\n",
       "    (left to right); when False, feature weight tables are printed\n",
       "    vertically (top to down). Default is True.\n",
       "\n",
       "highlight_spaces : bool or None, optional\n",
       "    Whether to highlight spaces in feature names. This is useful if\n",
       "    you work with text and have ngram features which may include spaces\n",
       "    at left or right. Default is None, meaning that the value used\n",
       "    is set automatically based on vectorizer and feature values.\n",
       "\n",
       "include_styles : bool\n",
       "    Most styles are inline, but some are included separately in <style> tag;\n",
       "    you can omit them by passing ``include_styles=False``. Default is True.\n",
       "\n",
       "**kwargs: dict\n",
       "    Keyword arguments. All keyword arguments are passed to\n",
       "    concrete explain_weights... implementations.\n",
       "\n",
       "Returns\n",
       "-------\n",
       "IPython.display.HTML\n",
       "    The result is printed in IPython notebook as an HTML widget.\n",
       "    If you need to display several explanations as an output of a single\n",
       "    cell, or if you want to display it from a function then use\n",
       "    IPython.display.display::\n",
       "\n",
       "        from IPython.display import display\n",
       "        display(eli5.show_weights(clf1))\n",
       "        display(eli5.show_weights(clf2))\n",
       "\u001b[1;31mFile:\u001b[0m      c:\\users\\user\\anaconda3\\envs\\demo2\\lib\\site-packages\\eli5\\ipython.py\n",
       "\u001b[1;31mType:\u001b[0m      function\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "?eli5.show_weights"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7919af87-c228-4598-a0a1-434a68de361c",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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