Recent Topic Sentiment Mix.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0929a19f",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Install libraries\n",
    "# https://docs.tweepy.org/en/stable/index.html\n",
    "# https://pypi.org/project/deep-translator/\n",
    "# https://github.com/bsolomon1124/demoji\n",
    "# https://pypi.org/project/textblob/\n",
    "# https://pypi.org/project/flair/\n",
    "\n",
    "%pip install tweepy\n",
    "%pip install tweepy[async]\n",
    "%pip install deep-translator\n",
    "%pip install demoji\n",
    "%pip install textblob \n",
    "%pip install flair"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "72436635",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2022-11-05 20:03:06,144 loading file C:\\Users\\ellie\\.flair\\models\\sentiment-en-mix-distillbert_4.pt\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[nltk_data] Downloading package vader_lexicon to\n",
      "[nltk_data]     C:\\Users\\ellie\\AppData\\Roaming\\nltk_data...\n",
      "[nltk_data]   Package vader_lexicon is already up-to-date!\n"
     ]
    }
   ],
   "source": [
    "# Set-up notebook\n",
    "import datetime\n",
    "from datetime import datetime, timedelta, date\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import json\n",
    "import tweepy\n",
    "from tweepy import asynchronous\n",
    "import time\n",
    "from deep_translator import GoogleTranslator\n",
    "import demoji\n",
    "from textblob import TextBlob\n",
    "import nltk\n",
    "from nltk.sentiment.vader import SentimentIntensityAnalyzer\n",
    "from flair.models import TextClassifier\n",
    "from flair.data import Sentence\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "nltk.download('vader_lexicon')\n",
    "\n",
    "format_time = '%Y-%m-%dT%H:%M:%SZ'\n",
    "def cp_transform(check_point):\n",
    "    check_point = str(check_point)[:-7].replace(' ','T') + 'Z'\n",
    "    check_point = datetime.strptime(check_point,format_time)\n",
    "    return check_point\n",
    "\n",
    "check_point = cp_transform(datetime.now() - timedelta(days=6))\n",
    "buffer = 0\n",
    "\n",
    "flair_model = TextClassifier.load('en-sentiment')\n",
    "def flair_prediction(x):\n",
    "    sentence = Sentence(x)\n",
    "    flair_model.predict(sentence)\n",
    "    score = sentence.labels[0]\n",
    "    if 'POSITIVE' in str(score):\n",
    "        return 1\n",
    "    elif 'NEGATIVE' in str(score):\n",
    "        return -1\n",
    "    else:\n",
    "        return 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "47e29592",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Authenticate Twitter dev account\n",
    "config_path = \"tweets\\config.json\"\n",
    "\n",
    "with open(config_path) as json_file:\n",
    "    config = json.load(json_file)\n",
    "\n",
    "bearer_token = config[\"bearer-token\"]\n",
    "client = tweepy.asynchronous.AsyncClient(bearer_token=bearer_token)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6ec3543e",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Set-up Twitter query\n",
    "keyword = input('Please enter keyword or hashtag to search: ')\n",
    "no_tweets = int(input ('Please enter how many tweets to analyze: '))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "c8bb560c",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Search recent tweets\n",
    "start = datetime.now()\n",
    "limit = int(np.ceil(no_tweets / 100))\n",
    "data = []\n",
    "\n",
    "while limit > 0:\n",
    "    response = await client.search_recent_tweets(keyword, tweet_fields=['created_at', 'lang', 'geo','author_id'], \n",
    "                                start_time=check_point,max_results=100)\n",
    "    data.extend(response.data)\n",
    "    time.sleep(30)\n",
    "    limit -= 1\n",
    "\n",
    "df = pd.DataFrame.from_dict(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "fd8ef463",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Remove duplicate entries\n",
    "df = df.drop_duplicates(['id'])\n",
    "df = df.sort_values(by=['created_at'], ascending=False)\n",
    "df = df.reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "790cc27c",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Checkpoint time stamp\n",
    "buffer = timedelta(seconds=1)\n",
    "check_point = cp_transform(df['created_at'][0])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "1010d900",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Translate Twitter data\n",
    "i = 0 # iterator\n",
    "text_translated = []\n",
    "\n",
    "for row in df.iterrows():\n",
    "    translated = GoogleTranslator(source='auto', target='en').translate(df.loc[i]['text'])\n",
    "    translated = demoji.replace_with_desc(translated)\n",
    "    text_translated.append(translated)\n",
    "    i += 1\n",
    "\n",
    "df['text_translated'] = text_translated  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "51da9749",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Process Twitter data\n",
    "i = 0 # iterator\n",
    "textblob_sentiment = []\n",
    "textblob_subjectivity = []\n",
    "nltk_sentiment = []\n",
    "flair_sentiment = []\n",
    "\n",
    "for row in df.iterrows():\n",
    "    \n",
    "    textblob = TextBlob(df.loc[i]['text_translated'])\n",
    "    nltk = SentimentIntensityAnalyzer().polarity_scores(df.loc[i]['text_translated'])\n",
    "    flair = flair_prediction(df.loc[i]['text_translated'])\n",
    "    \n",
    "    textblob_sentiment.append(textblob.sentiment.polarity)\n",
    "    textblob_subjectivity.append(textblob.subjectivity)\n",
    "    nltk_sentiment.append(nltk['compound'])\n",
    "    flair_sentiment.append(flair)\n",
    "   \n",
    "    i += 1\n",
    "\n",
    "df['textblob_sentiment'] = textblob_sentiment\n",
    "df['textblob_subjectivity'] = textblob_subjectivity\n",
    "df['nltk_sentiment'] = nltk_sentiment\n",
    "df['flair_sentiment'] = flair_sentiment"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "369c806f",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Calculate composite score + class\n",
    "i = 0 # iterator\n",
    "score = []\n",
    "score_class =[]\n",
    "\n",
    "for row in df.iterrows():\n",
    "\n",
    "    total_sentiment = df.loc[i]['textblob_sentiment'] + df.loc[i]['nltk_sentiment']\n",
    "    \n",
    "    if total_sentiment == 0:\n",
    "        sentiment\n",
    "    else:\n",
    "        sentiment = abs(total_sentiment / 2)\n",
    "    \n",
    "    sub_sentiment = sentiment + df.loc[i]['textblob_subjectivity']\n",
    "    polar_sentiment = sub_sentiment * df.loc[i]['flair_sentiment']\n",
    "    \n",
    "    if abs(polar_sentiment) > 1:\n",
    "        prefix = 'strong '\n",
    "    else: \n",
    "        prefix = ''\n",
    "        \n",
    "    if polar_sentiment > 0.15:\n",
    "        polarity = 'positive'\n",
    "    elif polar_sentiment < -0.15:\n",
    "        polarity = 'negative'\n",
    "    else:\n",
    "        polarity = 'neutral'\n",
    "        \n",
    "    polarity = prefix + polarity\n",
    "    \n",
    "    score.append(polar_sentiment)\n",
    "    score_class.append(polarity)\n",
    "    i += 1\n",
    "    \n",
    "df['polar_sentiment'] = score\n",
    "df['score_class'] = score_class"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "c178a485",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "datetime.timedelta(seconds=178, microseconds=997927)"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Set buffer\n",
    "end = datetime.now()\n",
    "buffer = end - start + timedelta(seconds=3)\n",
    "buffer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "86e32792",
   "metadata": {},
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
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     },
     "metadata": {},
     "output_type": "display_data"
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>index</th>\n",
       "      <th>author_id</th>\n",
       "      <th>created_at</th>\n",
       "      <th>edit_history_tweet_ids</th>\n",
       "      <th>geo</th>\n",
       "      <th>id</th>\n",
       "      <th>lang</th>\n",
       "      <th>text</th>\n",
       "      <th>text_translated</th>\n",
       "      <th>textblob_sentiment</th>\n",
       "      <th>textblob_subjectivity</th>\n",
       "      <th>nltk_sentiment</th>\n",
       "      <th>flair_sentiment</th>\n",
       "      <th>polar_sentiment</th>\n",
       "      <th>score_class</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>206</td>\n",
       "      <td>1585816319538171907</td>\n",
       "      <td>2022-11-06 03:04:48+00:00</td>\n",
       "      <td>[1589091400007954433]</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1589091400007954433</td>\n",
       "      <td>en</td>\n",
       "      <td>RT @iluminatibot: Bill Gates buying all sorts ...</td>\n",
       "      <td>RT @iluminatibot: Bill Gates buying all sorts ...</td>\n",
       "      <td>0.625000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>-1</td>\n",
       "      <td>-0.812500</td>\n",
       "      <td>negative</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>199</td>\n",
       "      <td>280021509</td>\n",
       "      <td>2022-11-06 03:04:48+00:00</td>\n",
       "      <td>[1589091402591485952]</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1589091402591485952</td>\n",
       "      <td>es</td>\n",
       "      <td>RT @PFifiona: López Obrador dijo que iba a inv...</td>\n",
       "      <td>RT @PFifiona: López Obrador said he was going ...</td>\n",
       "      <td>-0.155556</td>\n",
       "      <td>0.288889</td>\n",
       "      <td>0.4019</td>\n",
       "      <td>-1</td>\n",
       "      <td>-0.412061</td>\n",
       "      <td>negative</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>200</td>\n",
       "      <td>292109098</td>\n",
       "      <td>2022-11-06 03:04:48+00:00</td>\n",
       "      <td>[1589091402423881730]</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1589091402423881730</td>\n",
       "      <td>en</td>\n",
       "      <td>RT @Black90Prupayne: And of course they can as...</td>\n",
       "      <td>RT @Black90Prupayne: And of course they can as...</td>\n",
       "      <td>0.250000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>-0.5719</td>\n",
       "      <td>-1</td>\n",
       "      <td>-0.660950</td>\n",
       "      <td>negative</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>201</td>\n",
       "      <td>931312901192855552</td>\n",
       "      <td>2022-11-06 03:04:48+00:00</td>\n",
       "      <td>[1589091401807327232]</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1589091401807327232</td>\n",
       "      <td>pt</td>\n",
       "      <td>RT @kimpaim: Watcher Guru: Elon Musk confirma ...</td>\n",
       "      <td>RT @kimpaim: Watcher Guru: Elon Musk confirms ...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>1</td>\n",
       "      <td>0.160950</td>\n",
       "      <td>positive</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>202</td>\n",
       "      <td>147941363</td>\n",
       "      <td>2022-11-06 03:04:48+00:00</td>\n",
       "      <td>[1589091401723441152]</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1589091401723441152</td>\n",
       "      <td>es</td>\n",
       "      <td>RT @AlertaNews24: 🇺🇸 | AHORA: Elon Musk anunci...</td>\n",
       "      <td>RT @AlertaNews24: :flag: United States: | NOW:...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.6249</td>\n",
       "      <td>-1</td>\n",
       "      <td>-0.312450</td>\n",
       "      <td>negative</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>294</th>\n",
       "      <td>95</td>\n",
       "      <td>901848797621493761</td>\n",
       "      <td>2022-11-06 03:03:37+00:00</td>\n",
       "      <td>[1589091104661856256]</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1589091104661856256</td>\n",
       "      <td>en</td>\n",
       "      <td>RT @ZackBornstein: You simply must admire the ...</td>\n",
       "      <td>RT @ZackBornstein: You simply must admire the ...</td>\n",
       "      <td>0.100000</td>\n",
       "      <td>0.228571</td>\n",
       "      <td>0.2617</td>\n",
       "      <td>1</td>\n",
       "      <td>0.409421</td>\n",
       "      <td>positive</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>295</th>\n",
       "      <td>96</td>\n",
       "      <td>1582626825347674112</td>\n",
       "      <td>2022-11-06 03:03:37+00:00</td>\n",
       "      <td>[1589091104636702723]</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1589091104636702723</td>\n",
       "      <td>en</td>\n",
       "      <td>@CPamp7 APESCOIN-MEMECOIN OF KING ELON MUSK\\nR...</td>\n",
       "      <td>@CPamp7 APESCOIN-MEMECOIN OF KING ELON MUSK\\nR...</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>0.300000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>-1</td>\n",
       "      <td>-0.400000</td>\n",
       "      <td>negative</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>296</th>\n",
       "      <td>97</td>\n",
       "      <td>298292516</td>\n",
       "      <td>2022-11-06 03:03:37+00:00</td>\n",
       "      <td>[1589091103310872576]</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1589091103310872576</td>\n",
       "      <td>pt</td>\n",
       "      <td>RT @kimpaim: Watcher Guru: Elon Musk confirma ...</td>\n",
       "      <td>RT @kimpaim: Watcher Guru: Elon Musk confirms ...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>1</td>\n",
       "      <td>0.100000</td>\n",
       "      <td>neutral</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>297</th>\n",
       "      <td>98</td>\n",
       "      <td>1528757024007172101</td>\n",
       "      <td>2022-11-06 03:03:37+00:00</td>\n",
       "      <td>[1589091103277731840]</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1589091103277731840</td>\n",
       "      <td>en</td>\n",
       "      <td>RT @hpsromantic: Elon Musk should buy Quora an...</td>\n",
       "      <td>RT @hpsromantic: Elon Musk should buy Quora an...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>-1</td>\n",
       "      <td>-0.100000</td>\n",
       "      <td>neutral</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>298</th>\n",
       "      <td>99</td>\n",
       "      <td>1499022077751226373</td>\n",
       "      <td>2022-11-06 03:03:37+00:00</td>\n",
       "      <td>[1589091103080603648]</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1589091103080603648</td>\n",
       "      <td>en</td>\n",
       "      <td>@jack If only you hadn’t filled the entire com...</td>\n",
       "      <td>@jack If only you hadn’t filled the entire com...</td>\n",
       "      <td>0.014286</td>\n",
       "      <td>0.589286</td>\n",
       "      <td>-0.4215</td>\n",
       "      <td>-1</td>\n",
       "      <td>-0.792893</td>\n",
       "      <td>negative</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>299 rows × 15 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     index            author_id                created_at  \\\n",
       "0      206  1585816319538171907 2022-11-06 03:04:48+00:00   \n",
       "1      199            280021509 2022-11-06 03:04:48+00:00   \n",
       "2      200            292109098 2022-11-06 03:04:48+00:00   \n",
       "3      201   931312901192855552 2022-11-06 03:04:48+00:00   \n",
       "4      202            147941363 2022-11-06 03:04:48+00:00   \n",
       "..     ...                  ...                       ...   \n",
       "294     95   901848797621493761 2022-11-06 03:03:37+00:00   \n",
       "295     96  1582626825347674112 2022-11-06 03:03:37+00:00   \n",
       "296     97            298292516 2022-11-06 03:03:37+00:00   \n",
       "297     98  1528757024007172101 2022-11-06 03:03:37+00:00   \n",
       "298     99  1499022077751226373 2022-11-06 03:03:37+00:00   \n",
       "\n",
       "    edit_history_tweet_ids  geo                   id lang  \\\n",
       "0    [1589091400007954433]  NaN  1589091400007954433   en   \n",
       "1    [1589091402591485952]  NaN  1589091402591485952   es   \n",
       "2    [1589091402423881730]  NaN  1589091402423881730   en   \n",
       "3    [1589091401807327232]  NaN  1589091401807327232   pt   \n",
       "4    [1589091401723441152]  NaN  1589091401723441152   es   \n",
       "..                     ...  ...                  ...  ...   \n",
       "294  [1589091104661856256]  NaN  1589091104661856256   en   \n",
       "295  [1589091104636702723]  NaN  1589091104636702723   en   \n",
       "296  [1589091103310872576]  NaN  1589091103310872576   pt   \n",
       "297  [1589091103277731840]  NaN  1589091103277731840   en   \n",
       "298  [1589091103080603648]  NaN  1589091103080603648   en   \n",
       "\n",
       "                                                  text  \\\n",
       "0    RT @iluminatibot: Bill Gates buying all sorts ...   \n",
       "1    RT @PFifiona: López Obrador dijo que iba a inv...   \n",
       "2    RT @Black90Prupayne: And of course they can as...   \n",
       "3    RT @kimpaim: Watcher Guru: Elon Musk confirma ...   \n",
       "4    RT @AlertaNews24: 🇺🇸 | AHORA: Elon Musk anunci...   \n",
       "..                                                 ...   \n",
       "294  RT @ZackBornstein: You simply must admire the ...   \n",
       "295  @CPamp7 APESCOIN-MEMECOIN OF KING ELON MUSK\\nR...   \n",
       "296  RT @kimpaim: Watcher Guru: Elon Musk confirma ...   \n",
       "297  RT @hpsromantic: Elon Musk should buy Quora an...   \n",
       "298  @jack If only you hadn’t filled the entire com...   \n",
       "\n",
       "                                       text_translated  textblob_sentiment  \\\n",
       "0    RT @iluminatibot: Bill Gates buying all sorts ...            0.625000   \n",
       "1    RT @PFifiona: López Obrador said he was going ...           -0.155556   \n",
       "2    RT @Black90Prupayne: And of course they can as...            0.250000   \n",
       "3    RT @kimpaim: Watcher Guru: Elon Musk confirms ...            0.000000   \n",
       "4    RT @AlertaNews24: :flag: United States: | NOW:...            0.000000   \n",
       "..                                                 ...                 ...   \n",
       "294  RT @ZackBornstein: You simply must admire the ...            0.100000   \n",
       "295  @CPamp7 APESCOIN-MEMECOIN OF KING ELON MUSK\\nR...            0.200000   \n",
       "296  RT @kimpaim: Watcher Guru: Elon Musk confirms ...            0.000000   \n",
       "297  RT @hpsromantic: Elon Musk should buy Quora an...            0.000000   \n",
       "298  @jack If only you hadn’t filled the entire com...            0.014286   \n",
       "\n",
       "     textblob_subjectivity  nltk_sentiment  flair_sentiment  polar_sentiment  \\\n",
       "0                 0.500000          0.0000               -1        -0.812500   \n",
       "1                 0.288889          0.4019               -1        -0.412061   \n",
       "2                 0.500000         -0.5719               -1        -0.660950   \n",
       "3                 0.000000          0.0000                1         0.160950   \n",
       "4                 0.000000          0.6249               -1        -0.312450   \n",
       "..                     ...             ...              ...              ...   \n",
       "294               0.228571          0.2617                1         0.409421   \n",
       "295               0.300000          0.0000               -1        -0.400000   \n",
       "296               0.000000          0.0000                1         0.100000   \n",
       "297               0.000000          0.0000               -1        -0.100000   \n",
       "298               0.589286         -0.4215               -1        -0.792893   \n",
       "\n",
       "    score_class  \n",
       "0      negative  \n",
       "1      negative  \n",
       "2      negative  \n",
       "3      positive  \n",
       "4      negative  \n",
       "..          ...  \n",
       "294    positive  \n",
       "295    negative  \n",
       "296     neutral  \n",
       "297     neutral  \n",
       "298    negative  \n",
       "\n",
       "[299 rows x 15 columns]"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Create piechart\n",
    "strong_positive = len(df[df['score_class'] == 'strong positive'])\n",
    "positive = len(df[df['score_class'] == 'positive'])\n",
    "neutral = len(df[df['score_class'] == 'neutral'])\n",
    "negative = len(df[df['score_class'] == 'negative'])\n",
    "strong_negative = len(df[df['score_class'] == 'strong negative'])\n",
    "\n",
    "#labels = ['Positive ['+str(positive)+'%]' , 'Neutral ['+str(neutral)+'%]','Negative ['+str(negative)+'%]']\n",
    "labels = ['Strong Positive', 'Positive','Neutral','Negative','Strong Negative']\n",
    "sizes = [strong_positive, positive, neutral, negative, strong_negative]\n",
    "colors = ['green','yellowgreen','lightblue','pink','red']\n",
    "patches, texts = plt.pie(sizes,colors=colors, startangle=90)\n",
    "plt.style.use('default')\n",
    "plt.legend(labels)\n",
    "plt.title('Sentiment Analysis Result for keyword=  '+keyword+'')\n",
    "plt.axis('equal')\n",
    "plt.show()\n",
    "\n",
    "df"
   ]
  }
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