Text Analysis_JB.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 196,
   "id": "de505625",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[nltk_data] Downloading package wordnet to\n",
      "[nltk_data]     C:\\Users\\good5\\AppData\\Roaming\\nltk_data...\n",
      "[nltk_data]   Package wordnet is already up-to-date!\n"
     ]
    }
   ],
   "source": [
    "import nltk\n",
    "from nltk.tokenize import TreebankWordTokenizer\n",
    "from nltk.tokenize.treebank import TreebankWordDetokenizer\n",
    "from textblob import TextBlob, Word\n",
    "from nltk.corpus import stopwords\n",
    "import re\n",
    "import pandas as pd\n",
    "from collections import OrderedDict\n",
    "from operator import getitem\n",
    "nltk.download('wordnet')\n",
    "from nltk.corpus import wordnet as wn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "id": "744e159d",
   "metadata": {},
   "outputs": [],
   "source": [
    "stop_words = stopwords.words('english')\n",
    "stop_words.extend(['covid', 'virus', 'https', 'com', 'http', 'que', ' ', 'mierda', 'super', 'new', 'bbmp'])\n",
    "shortword = re.compile(r'\\W*\\b\\w{1,2}\\b')\n",
    "\n",
    "def lem(a):   \n",
    "    sent = TextBlob(a)\n",
    "    result = \" \".join([w.lemmatize() for w in sent.words])\n",
    "    return result   #lemmentization\n",
    "\n",
    "def process_text(a):\n",
    "    a = str(re.sub(\"\\S*\\d\\S*\", \"\", a).strip()) \n",
    "    a = a.lower()    \n",
    "    a = re.sub(r\"[^a-zA-Z0-9]\",\" \",a) #특수문자 제거\n",
    "    a = re.sub(\"([^\\x00-\\x7F])+\",\"\",a) #영어이외제거    \n",
    "    a = re.sub(\"(.)\\\\1{3,}\", \"\\\\1\", a) #긴 반복문 제거\n",
    "    a = re.sub('https?://\\S+|www\\.\\S+', '', a) #하이퍼링크 제거\n",
    "    a = shortword.sub(' ', a)    #짧은 단어 제거        \n",
    "    a = lem(a)    \n",
    "    tokenizer = TreebankWordTokenizer()\n",
    "    # tokenize texts\n",
    "    tokens = tokenizer.tokenize(a)  \n",
    "    \n",
    "    result = []\n",
    "    for b in tokens:\n",
    "        b = shortword.sub(' ', b)    #짧은 단어 제거  \n",
    "        if b not in stop_words:  #stopwords 제거\n",
    "            result.append(b)   \n",
    "    \n",
    "    return result\n",
    "\n",
    "def remove_emoji(string):\n",
    "    emoji_pattern = re.compile(\"[\"\n",
    "                               u\"\\U0001F600-\\U0001F64F\"  # emoticons\n",
    "                               u\"\\U0001F300-\\U0001F5FF\"  # symbols & pictographs\n",
    "                               u\"\\U0001F680-\\U0001F6FF\"  # transport & map symbols\n",
    "                               u\"\\U0001F1E0-\\U0001F1FF\"  # flags (iOS)\n",
    "                               u\"\\U00002500-\\U00002BEF\"  # chinese char\n",
    "                               u\"\\U00002702-\\U000027B0\"\n",
    "                               u\"\\U00002702-\\U000027B0\"\n",
    "                               u\"\\U000024C2-\\U0001F251\"\n",
    "                               u\"\\U0001f926-\\U0001f937\"\n",
    "                               u\"\\U00010000-\\U0010ffff\"\n",
    "                               u\"\\u2640-\\u2642\"\n",
    "                               u\"\\u2600-\\u2B55\"\n",
    "                               u\"\\u200d\"\n",
    "                               u\"\\u23cf\"\n",
    "                               u\"\\u23e9\"\n",
    "                               u\"\\u231a\"\n",
    "                               u\"\\ufe0f\"  # dingbats\n",
    "                               u\"\\u3030\"\n",
    "                               \"]+\", flags=re.UNICODE)\n",
    "    return emoji_pattern.sub(r'', string)\n",
    "\n",
    " #명사만 추출"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "id": "e7af4424",
   "metadata": {},
   "outputs": [],
   "source": [
    "raw_df = pd.read_csv('./data/vaccination_all_tweets.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "id": "804edaa9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'2020-12-12 11:55:28'"
      ]
     },
     "execution_count": 138,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "min(raw_df['date'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "id": "4f8fb7eb",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['process'] = ''\n",
    "# df_2021.process = df_2021.Content.apply(process_text)\n",
    "df.process = df.text.apply(process_text)\n",
    "\n",
    "df['pos'] = df['process'].apply(lambda x: nltk.pos_tag(x)) # token에 pos tagging \n",
    "\n",
    "NN_list = [] # 명사 리스트 생성\n",
    "for i in range(0, len(df['pos'])) :\n",
    "    NN_words = []\n",
    "    for word, pos in df['pos'].iloc[i]: \n",
    "        if 'NN' in pos: #pos에 nn 이 들어가 있으면 \n",
    "            NN_words.append(word)\n",
    "    NN_list.append(NN_words)    \n",
    "\n",
    "df['nn_list'] = NN_list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "id": "63db58dd",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'2020-12-12 11:55:28'"
      ]
     },
     "execution_count": 140,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "min(df['date'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "id": "b3ccd7a9",
   "metadata": {},
   "outputs": [],
   "source": [
    "mask_1 = (df['date'] >= '2020-12-12 00:00:00') & (df['date'] <= '2021-02-11 23:59:59')\n",
    "df1 = df.loc[mask_1]\n",
    "df1 = df1.reset_index()\n",
    "\n",
    "mask_2 = (df['date'] >= '2021-02-12 00:00:00') & (df['date'] <= '2021-05-11 23:59:59')\n",
    "df2 = df.loc[mask_2]\n",
    "df2 = df2.reset_index()\n",
    "\n",
    "mask_3 = (df['date'] >= '2021-05-12 00:00:00') & (df['date'] <= '2021-08-11 23:59:59')\n",
    "df3 = df.loc[mask_3]\n",
    "df3 = df3.reset_index()\n",
    "\n",
    "mask_4 = (df['date'] >= '2021-08-12 00:00:00') & (df['date'] <= '2021-12-31 23:59:59')\n",
    "df4 = df.loc[mask_4]\n",
    "df4 = df4.reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 193,
   "id": "e8499e01",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "7569 62149 94386 64103\n"
     ]
    }
   ],
   "source": [
    "print(len(df1), len(df2), len(df3), len(df4))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 194,
   "id": "82907243",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "129.85031047694542\n",
      "5.2973972783723084\n",
      "######\n",
      "124.69166036460764\n",
      "5.33356932533106\n",
      "######\n",
      "122.70802873307483\n",
      "5.665766109380629\n",
      "######\n",
      "126.51866527307614\n",
      "5.970313401868867\n",
      "######\n"
     ]
    }
   ],
   "source": [
    "for df in [df1, df2, df3, df4]:\n",
    "    length_arr = []\n",
    "    nn_arr = []\n",
    "    for i in range(len(df)):\n",
    "        length_arr.append(len(df['text'][i]))\n",
    "        nn_arr.append(len(df['nn_list'][i]))\n",
    "    print(sum(length_arr)/len(df))\n",
    "    print(sum(nn_arr)/len(df))\n",
    "    print(\"######\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "id": "f32fdf4e",
   "metadata": {},
   "outputs": [
    {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>index</th>\n",
       "      <th>id</th>\n",
       "      <th>user_name</th>\n",
       "      <th>user_location</th>\n",
       "      <th>user_description</th>\n",
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       "      <th>user_followers</th>\n",
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       "      <th>user_verified</th>\n",
       "      <th>date</th>\n",
       "      <th>text</th>\n",
       "      <th>hashtags</th>\n",
       "      <th>source</th>\n",
       "      <th>retweets</th>\n",
       "      <th>favorites</th>\n",
       "      <th>is_retweet</th>\n",
       "      <th>process</th>\n",
       "      <th>pos</th>\n",
       "      <th>nn_list</th>\n",
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       "  </thead>\n",
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       "      <td>True</td>\n",
       "      <td>2021-05-21 05:00:01</td>\n",
       "      <td>#Turkey seals deal for 60 mln more doses of #P...</td>\n",
       "      <td>['Turkey', 'PfizerBioNtech', 'vaccine']</td>\n",
       "      <td>TweetDeck</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>False</td>\n",
       "      <td>[turkey, seal, deal, mln, dos, pfizerbiontech,...</td>\n",
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       "      <td>[deal, mln, dos, pfizerbiontech, vaccine]</td>\n",
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       "      <td>iLyas BK</td>\n",
       "      <td>Montréal, Québec</td>\n",
       "      <td>I Build #Startups and Invest In People - Sr So...</td>\n",
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       "      <td>False</td>\n",
       "      <td>2021-05-21 00:20:55</td>\n",
       "      <td>Done. #PfizerBiontech https://t.co/9EKcNJXGNr</td>\n",
       "      <td>['PfizerBiontech']</td>\n",
       "      <td>Twitter for iPhone</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>False</td>\n",
       "      <td>[done, pfizerbiontech]</td>\n",
       "      <td>[(done, VBN), (pfizerbiontech, NN)]</td>\n",
       "      <td>[pfizerbiontech]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>69720</td>\n",
       "      <td>1395511714372784134</td>\n",
       "      <td>kkm</td>\n",
       "      <td>new jersey</td>\n",
       "      <td>real world nurse practitioner--mid-level--term...</td>\n",
       "      <td>2010-09-01 17:46:14</td>\n",
       "      <td>808</td>\n",
       "      <td>297</td>\n",
       "      <td>10160</td>\n",
       "      <td>False</td>\n",
       "      <td>2021-05-20 22:48:14</td>\n",
       "      <td>#Science is wonderful #CovidVaccine #Astrazene...</td>\n",
       "      <td>['Science', 'CovidVaccine', 'Astrazeneca', 'Pf...</td>\n",
       "      <td>Twitter for iPhone</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>False</td>\n",
       "      <td>[science, wonderful, covidvaccine, astrazeneca...</td>\n",
       "      <td>[(science, NN), (wonderful, JJ), (covidvaccine...</td>\n",
       "      <td>[science, covidvaccine, astrazeneca, pfizerbio...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>69721</td>\n",
       "      <td>1395471657603567617</td>\n",
       "      <td>Ms. Nony 💙</td>\n",
       "      <td>East Coast 🇺🇸</td>\n",
       "      <td>Love to laugh, good conversation, coffee, poli...</td>\n",
       "      <td>2021-01-09 15:17:26</td>\n",
       "      <td>8143</td>\n",
       "      <td>7415</td>\n",
       "      <td>19732</td>\n",
       "      <td>False</td>\n",
       "      <td>2021-05-20 20:09:04</td>\n",
       "      <td>I now have a houseful of half-vaxxed kiddos wi...</td>\n",
       "      <td>['PfizerBiontech']</td>\n",
       "      <td>Twitter Web App</td>\n",
       "      <td>0</td>\n",
       "      <td>16</td>\n",
       "      <td>False</td>\n",
       "      <td>[houseful, half, vaxxed, kiddos, pfizerbiontec...</td>\n",
       "      <td>[(houseful, JJ), (half, NN), (vaxxed, JJ), (ki...</td>\n",
       "      <td>[half, kiddos, june, household]</td>\n",
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       "      <th>4</th>\n",
       "      <td>69722</td>\n",
       "      <td>1395471023731007491</td>\n",
       "      <td>Ms. Nony 💙</td>\n",
       "      <td>East Coast 🇺🇸</td>\n",
       "      <td>Love to laugh, good conversation, coffee, poli...</td>\n",
       "      <td>2021-01-09 15:17:26</td>\n",
       "      <td>8143</td>\n",
       "      <td>7415</td>\n",
       "      <td>19732</td>\n",
       "      <td>False</td>\n",
       "      <td>2021-05-20 20:06:33</td>\n",
       "      <td>Fully vaxxed with #PfizerBiontech 🎉 https://t....</td>\n",
       "      <td>['PfizerBiontech']</td>\n",
       "      <td>Twitter Web App</td>\n",
       "      <td>3</td>\n",
       "      <td>154</td>\n",
       "      <td>False</td>\n",
       "      <td>[fully, vaxxed, pfizerbiontech]</td>\n",
       "      <td>[(fully, RB), (vaxxed, JJ), (pfizerbiontech, NN)]</td>\n",
       "      <td>[pfizerbiontech]</td>\n",
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       "      <th>94381</th>\n",
       "      <td>165511</td>\n",
       "      <td>1425268288414576642</td>\n",
       "      <td>VaxBLR</td>\n",
       "      <td>Bengaluru, India</td>\n",
       "      <td>Hourly updates on FREE and PAID 18+ and 45+ va...</td>\n",
       "      <td>2021-06-21 08:44:34</td>\n",
       "      <td>20</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>False</td>\n",
       "      <td>2021-08-11 01:30:14</td>\n",
       "      <td>18-44 #URBAN #Bengaluru #CovidVaccine Availabi...</td>\n",
       "      <td>['URBAN', 'Bengaluru', 'CovidVaccine', 'COVISH...</td>\n",
       "      <td>VaxBlr</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>False</td>\n",
       "      <td>[urban, bengaluru, covidvaccine, availability,...</td>\n",
       "      <td>[(urban, JJ), (bengaluru, NN), (covidvaccine, ...</td>\n",
       "      <td>[bengaluru, covidvaccine, availability, slot, ...</td>\n",
       "    </tr>\n",
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       "      <td>165512</td>\n",
       "      <td>1425254676803592193</td>\n",
       "      <td>Julianne Wiley</td>\n",
       "      <td>Peaceful hills, Upper East TN</td>\n",
       "      <td>Inveterate remedial catechist, Catholic factio...</td>\n",
       "      <td>2020-07-12 15:05:35</td>\n",
       "      <td>512</td>\n",
       "      <td>221</td>\n",
       "      <td>8564</td>\n",
       "      <td>False</td>\n",
       "      <td>2021-08-11 00:36:09</td>\n",
       "      <td>@Shuliicabrera @gregmendozave @telefenoticias ...</td>\n",
       "      <td>['SanLorenzo']</td>\n",
       "      <td>Twitter Web App</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "      <td>[shuliicabrera, gregmendozave, telefenoticias,...</td>\n",
       "      <td>[(shuliicabrera, NN), (gregmendozave, NN), (te...</td>\n",
       "      <td>[shuliicabrera, gregmendozave, telefenoticias,...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>94383</th>\n",
       "      <td>165513</td>\n",
       "      <td>1425250019154841604</td>\n",
       "      <td>NewsRadio - TNLRN</td>\n",
       "      <td>Colombo, Sri Lanka</td>\n",
       "      <td>As the News team of TNL Radio Network, we keep...</td>\n",
       "      <td>2010-03-10 10:10:19</td>\n",
       "      <td>4070</td>\n",
       "      <td>11</td>\n",
       "      <td>4</td>\n",
       "      <td>False</td>\n",
       "      <td>2021-08-11 00:17:39</td>\n",
       "      <td>A stock of 15,000 Russian manufactured Sputnik...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Twitter for Android</td>\n",
       "      <td>3</td>\n",
       "      <td>12</td>\n",
       "      <td>False</td>\n",
       "      <td>[stock, russian, manufactured, sputnik, vaccin...</td>\n",
       "      <td>[(stock, NN), (russian, NN), (manufactured, VB...</td>\n",
       "      <td>[stock, russian, vaccine, dos, lanka]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>94384</th>\n",
       "      <td>165514</td>\n",
       "      <td>1425246065729490945</td>\n",
       "      <td>Suriya</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2011-05-04 08:29:12</td>\n",
       "      <td>13</td>\n",
       "      <td>239</td>\n",
       "      <td>2312</td>\n",
       "      <td>False</td>\n",
       "      <td>2021-08-11 00:01:56</td>\n",
       "      <td>@sputnikvaccine Still why not #WHO approval fo...</td>\n",
       "      <td>['WHO', 'SputnikV']</td>\n",
       "      <td>Twitter for Android</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "      <td>[sputnikvaccine, still, approval, sputnikv]</td>\n",
       "      <td>[(sputnikvaccine, NN), (still, RB), (approval,...</td>\n",
       "      <td>[sputnikvaccine, approval, sputnikv]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>94385</th>\n",
       "      <td>165515</td>\n",
       "      <td>1425199419897614345</td>\n",
       "      <td>SAVIOUR SELASI AMENYEDZI</td>\n",
       "      <td>Keta, Ghana</td>\n",
       "      <td>Host of Morning Show on @Jubilee1069fm.  A dow...</td>\n",
       "      <td>2012-12-08 07:37:03</td>\n",
       "      <td>297</td>\n",
       "      <td>472</td>\n",
       "      <td>6068</td>\n",
       "      <td>False</td>\n",
       "      <td>2021-08-10 20:56:35</td>\n",
       "      <td>Tune in tomorrow morning and let's get in dept...</td>\n",
       "      <td>['AgyemanManuMustGo']</td>\n",
       "      <td>Twitter for Android</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>False</td>\n",
       "      <td>[tune, tomorrow, morning, let, get, depth, dis...</td>\n",
       "      <td>[(tune, NN), (tomorrow, NN), (morning, NN), (l...</td>\n",
       "      <td>[tune, tomorrow, morning, let, disection, parl...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>94386 rows × 20 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        index                   id                 user_name  \\\n",
       "0       69718  1395605274950471685       Hürriyet Daily News   \n",
       "1       69719  1395535037320376321                  iLyas BK   \n",
       "2       69720  1395511714372784134                       kkm   \n",
       "3       69721  1395471657603567617                Ms. Nony 💙   \n",
       "4       69722  1395471023731007491                Ms. Nony 💙   \n",
       "...       ...                  ...                       ...   \n",
       "94381  165511  1425268288414576642                    VaxBLR   \n",
       "94382  165512  1425254676803592193            Julianne Wiley   \n",
       "94383  165513  1425250019154841604         NewsRadio - TNLRN   \n",
       "94384  165514  1425246065729490945                    Suriya   \n",
       "94385  165515  1425199419897614345  SAVIOUR SELASI AMENYEDZI   \n",
       "\n",
       "                       user_location  \\\n",
       "0                           Istanbul   \n",
       "1                   Montréal, Québec   \n",
       "2                         new jersey   \n",
       "3                      East Coast 🇺🇸   \n",
       "4                      East Coast 🇺🇸   \n",
       "...                              ...   \n",
       "94381               Bengaluru, India   \n",
       "94382  Peaceful hills, Upper East TN   \n",
       "94383             Colombo, Sri Lanka   \n",
       "94384                            NaN   \n",
       "94385                    Keta, Ghana   \n",
       "\n",
       "                                        user_description         user_created  \\\n",
       "0      Turkey's English news source for more than 60 ...  2009-11-05 08:33:04   \n",
       "1      I Build #Startups and Invest In People - Sr So...  2008-07-17 00:32:03   \n",
       "2      real world nurse practitioner--mid-level--term...  2010-09-01 17:46:14   \n",
       "3      Love to laugh, good conversation, coffee, poli...  2021-01-09 15:17:26   \n",
       "4      Love to laugh, good conversation, coffee, poli...  2021-01-09 15:17:26   \n",
       "...                                                  ...                  ...   \n",
       "94381  Hourly updates on FREE and PAID 18+ and 45+ va...  2021-06-21 08:44:34   \n",
       "94382  Inveterate remedial catechist, Catholic factio...  2020-07-12 15:05:35   \n",
       "94383  As the News team of TNL Radio Network, we keep...  2010-03-10 10:10:19   \n",
       "94384                                                NaN  2011-05-04 08:29:12   \n",
       "94385  Host of Morning Show on @Jubilee1069fm.  A dow...  2012-12-08 07:37:03   \n",
       "\n",
       "       user_followers  user_friends  user_favourites  user_verified  \\\n",
       "0              279470            75                0           True   \n",
       "1                2551           942             2242          False   \n",
       "2                 808           297            10160          False   \n",
       "3                8143          7415            19732          False   \n",
       "4                8143          7415            19732          False   \n",
       "...               ...           ...              ...            ...   \n",
       "94381              20             0                0          False   \n",
       "94382             512           221             8564          False   \n",
       "94383            4070            11                4          False   \n",
       "94384              13           239             2312          False   \n",
       "94385             297           472             6068          False   \n",
       "\n",
       "                      date                                               text  \\\n",
       "0      2021-05-21 05:00:01  #Turkey seals deal for 60 mln more doses of #P...   \n",
       "1      2021-05-21 00:20:55      Done. #PfizerBiontech https://t.co/9EKcNJXGNr   \n",
       "2      2021-05-20 22:48:14  #Science is wonderful #CovidVaccine #Astrazene...   \n",
       "3      2021-05-20 20:09:04  I now have a houseful of half-vaxxed kiddos wi...   \n",
       "4      2021-05-20 20:06:33  Fully vaxxed with #PfizerBiontech 🎉 https://t....   \n",
       "...                    ...                                                ...   \n",
       "94381  2021-08-11 01:30:14  18-44 #URBAN #Bengaluru #CovidVaccine Availabi...   \n",
       "94382  2021-08-11 00:36:09  @Shuliicabrera @gregmendozave @telefenoticias ...   \n",
       "94383  2021-08-11 00:17:39  A stock of 15,000 Russian manufactured Sputnik...   \n",
       "94384  2021-08-11 00:01:56  @sputnikvaccine Still why not #WHO approval fo...   \n",
       "94385  2021-08-10 20:56:35  Tune in tomorrow morning and let's get in dept...   \n",
       "\n",
       "                                                hashtags               source  \\\n",
       "0                ['Turkey', 'PfizerBioNtech', 'vaccine']            TweetDeck   \n",
       "1                                     ['PfizerBiontech']   Twitter for iPhone   \n",
       "2      ['Science', 'CovidVaccine', 'Astrazeneca', 'Pf...   Twitter for iPhone   \n",
       "3                                     ['PfizerBiontech']      Twitter Web App   \n",
       "4                                     ['PfizerBiontech']      Twitter Web App   \n",
       "...                                                  ...                  ...   \n",
       "94381  ['URBAN', 'Bengaluru', 'CovidVaccine', 'COVISH...               VaxBlr   \n",
       "94382                                     ['SanLorenzo']      Twitter Web App   \n",
       "94383                                                NaN  Twitter for Android   \n",
       "94384                                ['WHO', 'SputnikV']  Twitter for Android   \n",
       "94385                              ['AgyemanManuMustGo']  Twitter for Android   \n",
       "\n",
       "       retweets  favorites  is_retweet  \\\n",
       "0             2          2       False   \n",
       "1             0          4       False   \n",
       "2             0          0       False   \n",
       "3             0         16       False   \n",
       "4             3        154       False   \n",
       "...         ...        ...         ...   \n",
       "94381         0          0       False   \n",
       "94382         0          1       False   \n",
       "94383         3         12       False   \n",
       "94384         0          1       False   \n",
       "94385         0          0       False   \n",
       "\n",
       "                                                 process  \\\n",
       "0      [turkey, seal, deal, mln, dos, pfizerbiontech,...   \n",
       "1                                 [done, pfizerbiontech]   \n",
       "2      [science, wonderful, covidvaccine, astrazeneca...   \n",
       "3      [houseful, half, vaxxed, kiddos, pfizerbiontec...   \n",
       "4                        [fully, vaxxed, pfizerbiontech]   \n",
       "...                                                  ...   \n",
       "94381  [urban, bengaluru, covidvaccine, availability,...   \n",
       "94382  [shuliicabrera, gregmendozave, telefenoticias,...   \n",
       "94383  [stock, russian, manufactured, sputnik, vaccin...   \n",
       "94384        [sputnikvaccine, still, approval, sputnikv]   \n",
       "94385  [tune, tomorrow, morning, let, get, depth, dis...   \n",
       "\n",
       "                                                     pos  \\\n",
       "0      [(turkey, JJ), (seal, JJ), (deal, NN), (mln, N...   \n",
       "1                    [(done, VBN), (pfizerbiontech, NN)]   \n",
       "2      [(science, NN), (wonderful, JJ), (covidvaccine...   \n",
       "3      [(houseful, JJ), (half, NN), (vaxxed, JJ), (ki...   \n",
       "4      [(fully, RB), (vaxxed, JJ), (pfizerbiontech, NN)]   \n",
       "...                                                  ...   \n",
       "94381  [(urban, JJ), (bengaluru, NN), (covidvaccine, ...   \n",
       "94382  [(shuliicabrera, NN), (gregmendozave, NN), (te...   \n",
       "94383  [(stock, NN), (russian, NN), (manufactured, VB...   \n",
       "94384  [(sputnikvaccine, NN), (still, RB), (approval,...   \n",
       "94385  [(tune, NN), (tomorrow, NN), (morning, NN), (l...   \n",
       "\n",
       "                                                 nn_list  \n",
       "0              [deal, mln, dos, pfizerbiontech, vaccine]  \n",
       "1                                       [pfizerbiontech]  \n",
       "2      [science, covidvaccine, astrazeneca, pfizerbio...  \n",
       "3                        [half, kiddos, june, household]  \n",
       "4                                       [pfizerbiontech]  \n",
       "...                                                  ...  \n",
       "94381  [bengaluru, covidvaccine, availability, slot, ...  \n",
       "94382  [shuliicabrera, gregmendozave, telefenoticias,...  \n",
       "94383              [stock, russian, vaccine, dos, lanka]  \n",
       "94384               [sputnikvaccine, approval, sputnikv]  \n",
       "94385  [tune, tomorrow, morning, let, disection, parl...  \n",
       "\n",
       "[94386 rows x 20 columns]"
      ]
     },
     "execution_count": 142,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "528fe656",
   "metadata": {},
   "source": [
    "## 단어 빈도수 확인"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "id": "caa45ddc",
   "metadata": {},
   "outputs": [],
   "source": [
    "word_list_1 = []\n",
    "for i in range(len(df1)):\n",
    "    data = df1['nn_list'][i]\n",
    "    if len(df1['nn_list'][i])>0:\n",
    "        word_list_1.extend(data)\n",
    "\n",
    "word_list_2 = []\n",
    "for i in range(len(df2)):\n",
    "    data = df2['nn_list'][i]\n",
    "    if len(df2['nn_list'][i])>0:\n",
    "        word_list_2.extend(data)\n",
    "        \n",
    "word_list_3= []\n",
    "for i in range(len(df3)):\n",
    "    data = df3['nn_list'][i]\n",
    "    if len(df3['nn_list'][i])>0:\n",
    "        word_list_3.extend(data)\n",
    "        \n",
    "word_list_4 = []\n",
    "for i in range(len(df4)):\n",
    "    data = df4['nn_list'][i]\n",
    "    if len(df4['nn_list'][i])>0:\n",
    "        word_list_4.extend(data)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c270159c",
   "metadata": {},
   "source": [
    "## 문장 길이 확인"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "id": "27a6788e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[('vaccine', 3458), ('pfizerbiontech', 2176), ('pfizer', 945), ('moderna', 487), ('vaccination', 484), ('covidvaccine', 482), ('today', 472), ('coronavirus', 369), ('people', 345), ('day', 328), ('biontech', 253), ('sputnikv', 251), ('amp', 229), ('pfizervaccine', 223), ('health', 221), ('week', 216), ('dos', 215), ('covaxin', 201), ('use', 192), ('dose', 187)]\n"
     ]
    }
   ],
   "source": [
    "from collections import Counter\n",
    "print(Counter(word_list_1).most_common(20))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "id": "e72b030a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[('vaccine', 22625), ('moderna', 11820), ('covaxin', 9355), ('sputnikv', 4757), ('today', 3951), ('pfizer', 3686), ('shot', 3221), ('vaccination', 2992), ('russia', 2557), ('india', 2423), ('day', 2259), ('people', 2114), ('amp', 2022), ('covidvaccine', 1999), ('sinovac', 1959), ('sinopharm', 1839), ('china', 1823), ('case', 1822), ('dos', 1719), ('canada', 1653)]\n"
     ]
    }
   ],
   "source": [
    "print(Counter(word_list_2).most_common(20))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "id": "a0320631",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[('covaxin', 41483), ('vaccine', 21315), ('slot', 19547), ('age', 17508), ('moderna', 11517), ('hospital', 10651), ('dose', 7243), ('pfizer', 5176), ('sinovac', 4362), ('covishield', 4239), ('vaccination', 4229), ('date', 4161), ('sputnikv', 3999), ('pincode', 3856), ('apollo', 3843), ('fee', 3732), ('today', 3424), ('covidvaccine', 3423), ('india', 3399), ('sinopharm', 3217)]\n"
     ]
    }
   ],
   "source": [
    "print(Counter(word_list_3).most_common(20))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "id": "ea37b5fa",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[('covaxin', 23522), ('age', 16703), ('slot', 16464), ('vaccine', 14893), ('dose', 10593), ('moderna', 9506), ('pincode', 8489), ('date', 7929), ('fee', 7763), ('covidvaccine', 5968), ('covishield', 5703), ('availability', 5126), ('bengaluru', 5086), ('pfizer', 4770), ('sep', 3818), ('booster', 3289), ('approval', 2965), ('bharatbiotech', 2459), ('health', 2300), ('shot', 2164)]\n"
     ]
    }
   ],
   "source": [
    "print(Counter(word_list_4).most_common(20))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a3c8fadc",
   "metadata": {},
   "source": [
    "## 데이터셋에 대한 출력값 차이 비교"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "id": "07f12fcf",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: senticnet in c:\\users\\good5\\anaconda3\\lib\\site-packages (1.6)\n"
     ]
    }
   ],
   "source": [
    "!pip install senticnet"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 161,
   "id": "64678432",
   "metadata": {},
   "outputs": [],
   "source": [
    "from senticnet.senticnet import SenticNet\n",
    "from nltk.sentiment.vader import SentimentIntensityAnalyzer\n",
    "\n",
    "from nltk.corpus import sentiwordnet as swn\n",
    "\n",
    "vader = SentimentIntensityAnalyzer()\n",
    "sn = SenticNet()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 176,
   "id": "17f239d0",
   "metadata": {},
   "outputs": [],
   "source": [
    "senticnet_dict1 = dict()\n",
    "vader_dict1 = dict()\n",
    "for i in range(len(df1)):\n",
    "    sentence = df1['nn_list'][i]\n",
    "    for word in sentence:\n",
    "        try:\n",
    "            concept_info = sn.concept(word)\n",
    "            if word in senticnet_dict1:\n",
    "                senticnet_dict1[word]['count']+=1\n",
    "            else:\n",
    "                senticnet_dict1[word] = {'value':concept_info['polarity_value'],'count':1}\n",
    "        except KeyError as e:\n",
    "            pass\n",
    "        try:\n",
    "            if word in vader.lexicon:\n",
    "                if word in vader_dict1:\n",
    "                    vader_dict1[word]['count']+=1\n",
    "                else:\n",
    "                    vader_dict1[word] = {'value':vader.lexicon[word], 'count':1}\n",
    "        except KeyError as e:\n",
    "            pass\n",
    "\n",
    "senticnet_dict1 = OrderedDict(sorted(senticnet_dict1.items(), reverse=True,\n",
    "       key = lambda x: getitem(x[1], 'count')))\n",
    "vader_dict1 = OrderedDict(sorted(vader_dict1.items(), reverse=True,\n",
    "       key = lambda x: getitem(x[1], 'count')))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 181,
   "id": "12215b34",
   "metadata": {},
   "outputs": [],
   "source": [
    "senticnet_dict2 = dict()\n",
    "vader_dict2 = dict()\n",
    "for i in range(len(df2)):\n",
    "    sentence = df2['nn_list'][i]\n",
    "    for word in sentence:\n",
    "        try:\n",
    "            concept_info = sn.concept(word)\n",
    "            if word in senticnet_dict2:\n",
    "                senticnet_dict2[word]['count']+=1\n",
    "            else:\n",
    "                senticnet_dict2[word] = {'value':concept_info['polarity_value'],'count':1}\n",
    "        except KeyError as e:\n",
    "            pass\n",
    "        try:\n",
    "            if word in vader.lexicon:\n",
    "                if word in vader_dict2:\n",
    "                    vader_dict2[word]['count']+=1\n",
    "                else:\n",
    "                    vader_dict2[word] = {'value':vader.lexicon[word], 'count':1}\n",
    "        except KeyError as e:\n",
    "            pass\n",
    "        \n",
    "senticnet_dict2 = OrderedDict(sorted(senticnet_dict2.items(), reverse=True,\n",
    "       key = lambda x: getitem(x[1], 'count')))\n",
    "vader_dict2 = OrderedDict(sorted(vader_dict2.items(), reverse=True,\n",
    "       key = lambda x: getitem(x[1], 'count')))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 182,
   "id": "3de44bad",
   "metadata": {},
   "outputs": [],
   "source": [
    "senticnet_dict3 = dict()\n",
    "vader_dict3 = dict()\n",
    "for i in range(len(df3)):\n",
    "    sentence = df3['nn_list'][i]\n",
    "    for word in sentence:\n",
    "        try:\n",
    "            concept_info = sn.concept(word)\n",
    "            if word in senticnet_dict3:\n",
    "                senticnet_dict3[word]['count']+=1\n",
    "            else:\n",
    "                senticnet_dict3[word] = {'value':concept_info['polarity_value'],'count':1}\n",
    "        except KeyError as e:\n",
    "            pass\n",
    "        try:\n",
    "            if word in vader.lexicon:\n",
    "                if word in vader_dict3:\n",
    "                    vader_dict3[word]['count']+=1\n",
    "                else:\n",
    "                    vader_dict3[word] = {'value':vader.lexicon[word], 'count':1}\n",
    "        except KeyError as e:\n",
    "            pass\n",
    "        \n",
    "senticnet_dict3 = OrderedDict(sorted(senticnet_dict3.items(), reverse=True,\n",
    "       key = lambda x: getitem(x[1], 'count')))\n",
    "vader_dict3 = OrderedDict(sorted(vader_dict3.items(), reverse=True,\n",
    "       key = lambda x: getitem(x[1], 'count')))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 183,
   "id": "1b8acf61",
   "metadata": {},
   "outputs": [],
   "source": [
    "senticnet_dict4 = dict()\n",
    "vader_dict4 = dict()\n",
    "for i in range(len(df4)):\n",
    "    sentence = df4['nn_list'][i]\n",
    "    for word in sentence:\n",
    "        try:\n",
    "            concept_info = sn.concept(word)\n",
    "            if word in senticnet_dict4:\n",
    "                senticnet_dict4[word]['count']+=1\n",
    "            else:\n",
    "                senticnet_dict4[word] = {'value':concept_info['polarity_value'],'count':1}\n",
    "        except KeyError as e:\n",
    "            pass\n",
    "        try:\n",
    "            if word in vader.lexicon:\n",
    "                if word in vader_dict4:\n",
    "                    vader_dict4[word]['count']+=1\n",
    "                else:\n",
    "                    vader_dict4[word] = {'value':vader.lexicon[word], 'count':1}\n",
    "        except KeyError as e:\n",
    "            pass\n",
    "        \n",
    "senticnet_dict4 = OrderedDict(sorted(senticnet_dict4.items(), reverse=True,\n",
    "       key = lambda x: getitem(x[1], 'count')))\n",
    "vader_dict4 = OrderedDict(sorted(vader_dict4.items(), reverse=True,\n",
    "       key = lambda x: getitem(x[1], 'count')))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 192,
   "id": "77883539",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "OrderedDict([('approval', {'value': 2.1, 'count': 2965}),\n",
       "             ('emergency', {'value': -1.6, 'count': 1466}),\n",
       "             ('please', {'value': 1.3, 'count': 568}),\n",
       "             ('miracle', {'value': 2.8, 'count': 465}),\n",
       "             ('death', {'value': -2.9, 'count': 464}),\n",
       "             ('thank', {'value': 1.5, 'count': 457}),\n",
       "             ('risk', {'value': -1.1, 'count': 416}),\n",
       "             ('thanks', {'value': 1.9, 'count': 379}),\n",
       "             ('congratulation', {'value': 2.9, 'count': 374}),\n",
       "             ('share', {'value': 1.2, 'count': 351}),\n",
       "             ('safety', {'value': 1.8, 'count': 301}),\n",
       "             ('approves', {'value': 1.7, 'count': 295}),\n",
       "             ('help', {'value': 1.7, 'count': 292}),\n",
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       "             ('desire', {'value': 1.7, 'count': 2}),\n",
       "             ('freeze', {'value': 0.2, 'count': 2}),\n",
       "             ('pardon', {'value': 1.3, 'count': 2}),\n",
       "             ('rude', {'value': -2.0, 'count': 2}),\n",
       "             ('derail', {'value': -1.2, 'count': 2}),\n",
       "             ('low', {'value': -1.1, 'count': 2}),\n",
       "             ('arrogance', {'value': -2.4, 'count': 2}),\n",
       "             ('shakeout', {'value': -1.3, 'count': 2}),\n",
       "             ('disadvantaged', {'value': -1.7, 'count': 2}),\n",
       "             ('litigation', {'value': -0.8, 'count': 2}),\n",
       "             ('critic', {'value': -1.1, 'count': 2}),\n",
       "             ('downside', {'value': -1.0, 'count': 1}),\n",
       "             ('bothering', {'value': -1.6, 'count': 1}),\n",
       "             ('dumbass', {'value': -2.6, 'count': 1}),\n",
       "             ('destroyer', {'value': -2.0, 'count': 1}),\n",
       "             ('elation', {'value': 1.5, 'count': 1}),\n",
       "             ('bfd', {'value': -2.7, 'count': 1}),\n",
       "             ('coward', {'value': -2.0, 'count': 1}),\n",
       "             ('goddamn', {'value': -2.1, 'count': 1}),\n",
       "             ('intense', {'value': 0.3, 'count': 1}),\n",
       "             ('reassure', {'value': 1.4, 'count': 1}),\n",
       "             ('prosecution', {'value': -2.2, 'count': 1}),\n",
       "             ('supremacist', {'value': 0.5, 'count': 1}),\n",
       "             ('compassion', {'value': 2.0, 'count': 1}),\n",
       "             ('resigns', {'value': -1.3, 'count': 1}),\n",
       "             ('stupid', {'value': -2.4, 'count': 1}),\n",
       "             ('certainty', {'value': 1.0, 'count': 1}),\n",
       "             ('spark', {'value': 0.9, 'count': 1}),\n",
       "             ('beating', {'value': -2.0, 'count': 1}),\n",
       "             ('overjoyed', {'value': 2.7, 'count': 1}),\n",
       "             ('beaten', {'value': -1.8, 'count': 1}),\n",
       "             ('bankrupt', {'value': -2.6, 'count': 1}),\n",
       "             ('fond', {'value': 1.9, 'count': 1}),\n",
       "             ('helplessness', {'value': -2.1, 'count': 1}),\n",
       "             ('disadvantage', {'value': -1.8, 'count': 1}),\n",
       "             ('offense', {'value': -1.0, 'count': 1}),\n",
       "             ('boredom', {'value': -1.3, 'count': 1}),\n",
       "             ('wrong', {'value': -2.1, 'count': 1}),\n",
       "             ('lover', {'value': 2.8, 'count': 1}),\n",
       "             ('unaware', {'value': -0.8, 'count': 1}),\n",
       "             ('immortal', {'value': 1.0, 'count': 1}),\n",
       "             ('convincing', {'value': 1.7, 'count': 1}),\n",
       "             ('painful', {'value': -1.9, 'count': 1}),\n",
       "             ('encourages', {'value': 1.9, 'count': 1}),\n",
       "             ('alive', {'value': 1.6, 'count': 1}),\n",
       "             ('heh', {'value': -0.6, 'count': 1}),\n",
       "             ('swak', {'value': 2.3, 'count': 1}),\n",
       "             ('freak', {'value': -1.9, 'count': 1}),\n",
       "             ('vile', {'value': -3.1, 'count': 1}),\n",
       "             ('shameful', {'value': -2.2, 'count': 1}),\n",
       "             ('kinder', {'value': 2.2, 'count': 1}),\n",
       "             ('dullness', {'value': -1.4, 'count': 1}),\n",
       "             ('fraudster', {'value': -2.5, 'count': 1}),\n",
       "             ('vulnerable', {'value': -0.9, 'count': 1}),\n",
       "             ('adequate', {'value': 0.9, 'count': 1}),\n",
       "             ('sweetie', {'value': 2.2, 'count': 1}),\n",
       "             ('praying', {'value': 1.5, 'count': 1}),\n",
       "             ('selfish', {'value': -2.1, 'count': 1}),\n",
       "             ('vital', {'value': 1.2, 'count': 1}),\n",
       "             ('harmless', {'value': 1.0, 'count': 1}),\n",
       "             ('fantastic', {'value': 2.6, 'count': 1}),\n",
       "             ('pretty', {'value': 2.2, 'count': 1}),\n",
       "             ('enthusiast', {'value': 1.5, 'count': 1}),\n",
       "             ('lone', {'value': -1.1, 'count': 1}),\n",
       "             ('stronghold', {'value': 0.5, 'count': 1}),\n",
       "             ('lifesaver', {'value': 2.8, 'count': 1}),\n",
       "             ('ignores', {'value': -1.1, 'count': 1}),\n",
       "             ('sob', {'value': -1.0, 'count': 1}),\n",
       "             ('dead', {'value': -3.3, 'count': 1}),\n",
       "             ('greet', {'value': 1.3, 'count': 1}),\n",
       "             ('crush', {'value': -0.6, 'count': 1}),\n",
       "             ('stimulates', {'value': 1.0, 'count': 1}),\n",
       "             ('disgusting', {'value': -2.4, 'count': 1}),\n",
       "             ('cheat', {'value': -2.0, 'count': 1}),\n",
       "             ('prizer', {'value': 1.0, 'count': 1}),\n",
       "             ('clean', {'value': 1.7, 'count': 1}),\n",
       "             ('rape', {'value': -3.7, 'count': 1}),\n",
       "             ('battleground', {'value': -1.7, 'count': 1}),\n",
       "             ('screaming', {'value': -1.6, 'count': 1}),\n",
       "             ('lose', {'value': -1.7, 'count': 1}),\n",
       "             ('masterpiece', {'value': 3.1, 'count': 1}),\n",
       "             ('dehumanize', {'value': -1.8, 'count': 1}),\n",
       "             ('impressive', {'value': 2.3, 'count': 1}),\n",
       "             ('heaven', {'value': 2.3, 'count': 1}),\n",
       "             ('celebrates', {'value': 2.7, 'count': 1}),\n",
       "             ('worrying', {'value': -1.4, 'count': 1}),\n",
       "             ('hopeful', {'value': 2.3, 'count': 1}),\n",
       "             ('desperate', {'value': -1.3, 'count': 1}),\n",
       "             ('indignation', {'value': -2.4, 'count': 1}),\n",
       "             ('deprivation', {'value': -1.8, 'count': 1}),\n",
       "             ('satisfy', {'value': 2.0, 'count': 1}),\n",
       "             ('excellency', {'value': 2.5, 'count': 1}),\n",
       "             ('significance', {'value': 1.1, 'count': 1}),\n",
       "             ('tragic', {'value': -2.0, 'count': 1}),\n",
       "             ('criticizes', {'value': -1.4, 'count': 1}),\n",
       "             ('readiness', {'value': 1.0, 'count': 1}),\n",
       "             ('spite', {'value': -2.4, 'count': 1}),\n",
       "             ('encouragement', {'value': 1.8, 'count': 1}),\n",
       "             ('mature', {'value': 1.8, 'count': 1}),\n",
       "             ('grime', {'value': -1.5, 'count': 1}),\n",
       "             ('enjoyment', {'value': 2.6, 'count': 1}),\n",
       "             ('apologise', {'value': 1.6, 'count': 1}),\n",
       "             ('secures', {'value': 1.3, 'count': 1}),\n",
       "             ('paranoia', {'value': -1.0, 'count': 1}),\n",
       "             ('craziness', {'value': -1.6, 'count': 1}),\n",
       "             ('better', {'value': 1.9, 'count': 1}),\n",
       "             ('benign', {'value': 1.3, 'count': 1}),\n",
       "             ('postpone', {'value': -0.9, 'count': 1}),\n",
       "             ('restores', {'value': 1.2, 'count': 1}),\n",
       "             ('peacekeeper', {'value': 1.6, 'count': 1}),\n",
       "             ('boycotting', {'value': -1.7, 'count': 1}),\n",
       "             ('indecision', {'value': -0.8, 'count': 1}),\n",
       "             ('trap', {'value': -1.3, 'count': 1}),\n",
       "             ('contagion', {'value': -2.0, 'count': 1}),\n",
       "             ('irritability', {'value': -1.4, 'count': 1}),\n",
       "             ('snob', {'value': -2.0, 'count': 1}),\n",
       "             ('bold', {'value': 1.6, 'count': 1}),\n",
       "             ('lmfao', {'value': 2.5, 'count': 1}),\n",
       "             ('tenderness', {'value': 1.8, 'count': 1}),\n",
       "             ('degrade', {'value': -1.9, 'count': 1}),\n",
       "             ('puke', {'value': -2.4, 'count': 1}),\n",
       "             ('sorrow', {'value': -2.4, 'count': 1}),\n",
       "             ('dirt', {'value': -1.4, 'count': 1}),\n",
       "             ('honesty', {'value': 2.2, 'count': 1}),\n",
       "             ('paranoid', {'value': -1.0, 'count': 1}),\n",
       "             ('lethargic', {'value': -1.2, 'count': 1}),\n",
       "             ('distress', {'value': -2.4, 'count': 1}),\n",
       "             ('punishment', {'value': -2.2, 'count': 1}),\n",
       "             ('euphoria', {'value': 3.3, 'count': 1}),\n",
       "             ('shocking', {'value': -1.7, 'count': 1}),\n",
       "             ('approved', {'value': 1.8, 'count': 1}),\n",
       "             ('threatens', {'value': -1.6, 'count': 1}),\n",
       "             ('freebie', {'value': 1.8, 'count': 1}),\n",
       "             ('wreck', {'value': -1.9, 'count': 1}),\n",
       "             ('penalty', {'value': -2.0, 'count': 1}),\n",
       "             ('easier', {'value': 1.8, 'count': 1}),\n",
       "             ('cock', {'value': -0.6, 'count': 1}),\n",
       "             ('undermine', {'value': -1.2, 'count': 1}),\n",
       "             ('contradiction', {'value': -1.0, 'count': 1}),\n",
       "             ('rotten', {'value': -2.3, 'count': 1}),\n",
       "             ('hugging', {'value': 1.8, 'count': 1}),\n",
       "             ('scapegoat', {'value': -1.7, 'count': 1}),\n",
       "             ('fury', {'value': -2.7, 'count': 1}),\n",
       "             ('heartfelt', {'value': 2.5, 'count': 1}),\n",
       "             ('discouraged', {'value': -1.7, 'count': 1}),\n",
       "             ('ready', {'value': 1.5, 'count': 1}),\n",
       "             ('exploit', {'value': -0.4, 'count': 1}),\n",
       "             ('sweet', {'value': 2.0, 'count': 1}),\n",
       "             ('revenge', {'value': -2.4, 'count': 1}),\n",
       "             ('powerless', {'value': -2.2, 'count': 1}),\n",
       "             ('decay', {'value': -1.7, 'count': 1}),\n",
       "             ('criticize', {'value': -1.6, 'count': 1}),\n",
       "             ('perfection', {'value': 2.7, 'count': 1}),\n",
       "             ('funnier', {'value': 1.7, 'count': 1}),\n",
       "             ('impatience', {'value': -1.8, 'count': 1}),\n",
       "             ('optimist', {'value': 2.4, 'count': 1}),\n",
       "             ('freed', {'value': 1.7, 'count': 1}),\n",
       "             ('stink', {'value': -1.7, 'count': 1}),\n",
       "             ('flawless', {'value': 2.3, 'count': 1}),\n",
       "             ('distrust', {'value': -1.8, 'count': 1}),\n",
       "             ('confident', {'value': 2.2, 'count': 1}),\n",
       "             ('frustrating', {'value': -1.9, 'count': 1}),\n",
       "             ('helpful', {'value': 1.8, 'count': 1}),\n",
       "             ('strongman', {'value': 0.7, 'count': 1}),\n",
       "             ('remorse', {'value': -1.1, 'count': 1}),\n",
       "             ('numb', {'value': -1.4, 'count': 1}),\n",
       "             ('stealth', {'value': -0.3, 'count': 1}),\n",
       "             ('stench', {'value': -2.3, 'count': 1}),\n",
       "             ('hunger', {'value': -1.0, 'count': 1}),\n",
       "             ('confront', {'value': -0.7, 'count': 1}),\n",
       "             ('bitching', {'value': -1.1, 'count': 1}),\n",
       "             ('exploration', {'value': 0.9, 'count': 1}),\n",
       "             ('astound', {'value': 1.7, 'count': 1}),\n",
       "             ('thievery', {'value': -2.0, 'count': 1}),\n",
       "             ('craze', {'value': -0.6, 'count': 1}),\n",
       "             ('disappoints', {'value': -1.6, 'count': 1}),\n",
       "             ('blaming', {'value': -2.2, 'count': 1}),\n",
       "             ('safeguard', {'value': 1.6, 'count': 1}),\n",
       "             ('despise', {'value': -1.4, 'count': 1}),\n",
       "             ('sympathy', {'value': 1.5, 'count': 1}),\n",
       "             ('divine', {'value': 2.6, 'count': 1}),\n",
       "             ('sparkle', {'value': 1.8, 'count': 1}),\n",
       "             ('fearful', {'value': -2.2, 'count': 1}),\n",
       "             ('optimism', {'value': 2.5, 'count': 1}),\n",
       "             ('fascist', {'value': -2.6, 'count': 1}),\n",
       "             ('hurray', {'value': 2.7, 'count': 1}),\n",
       "             ('partying', {'value': 1.6, 'count': 1}),\n",
       "             ('energetic', {'value': 1.9, 'count': 1}),\n",
       "             ('challenger', {'value': 0.5, 'count': 1}),\n",
       "             ('fav', {'value': 2.0, 'count': 1}),\n",
       "             ('shy', {'value': -1.0, 'count': 1}),\n",
       "             ('totalitarianism', {'value': -2.7, 'count': 1}),\n",
       "             ('excel', {'value': 2.0, 'count': 1}),\n",
       "             ('avoids', {'value': -0.7, 'count': 1}),\n",
       "             ('richest', {'value': 2.4, 'count': 1}),\n",
       "             ('disagree', {'value': -1.6, 'count': 1}),\n",
       "             ('cruelty', {'value': -2.9, 'count': 1}),\n",
       "             ('worsen', {'value': -2.3, 'count': 1}),\n",
       "             ('playful', {'value': 1.9, 'count': 1}),\n",
       "             ('wimp', {'value': -1.4, 'count': 1}),\n",
       "             ('curse', {'value': -2.5, 'count': 1}),\n",
       "             ('snafu', {'value': -2.5, 'count': 1}),\n",
       "             ('misread', {'value': -1.1, 'count': 1}),\n",
       "             ('jewel', {'value': 1.5, 'count': 1}),\n",
       "             ('doomsayers', {'value': -1.7, 'count': 1}),\n",
       "             ('perjury', {'value': -1.9, 'count': 1}),\n",
       "             ('dynamite', {'value': 0.7, 'count': 1}),\n",
       "             ('warming', {'value': 0.6, 'count': 1}),\n",
       "             ('wiser', {'value': 1.2, 'count': 1}),\n",
       "             ('shamelessness', {'value': -1.4, 'count': 1}),\n",
       "             ('fiasco', {'value': -2.3, 'count': 1}),\n",
       "             ('ineffectiveness', {'value': -1.3, 'count': 1}),\n",
       "             ('tantrum', {'value': -1.8, 'count': 1}),\n",
       "             ('harassment', {'value': -2.5, 'count': 1}),\n",
       "             ('superiority', {'value': 1.4, 'count': 1}),\n",
       "             ('bizarre', {'value': -1.3, 'count': 1}),\n",
       "             ('meaningless', {'value': -1.9, 'count': 1}),\n",
       "             ('intimidate', {'value': -0.8, 'count': 1}),\n",
       "             ('wanker', {'value': -2.5, 'count': 1}),\n",
       "             ('delayed', {'value': -0.9, 'count': 1}),\n",
       "             ('frenzy', {'value': -1.3, 'count': 1}),\n",
       "             ('engage', {'value': 1.4, 'count': 1}),\n",
       "             ('comprehensive', {'value': 1.0, 'count': 1}),\n",
       "             ('agony', {'value': -1.8, 'count': 1}),\n",
       "             ('hypocritical', {'value': -2.0, 'count': 1}),\n",
       "             ('satisfactory', {'value': 1.5, 'count': 1}),\n",
       "             ('postponing', {'value': -0.5, 'count': 1}),\n",
       "             ('disappoint', {'value': -1.7, 'count': 1}),\n",
       "             ('compassionate', {'value': 2.2, 'count': 1}),\n",
       "             ('competent', {'value': 1.3, 'count': 1}),\n",
       "             ('rejecting', {'value': -2.0, 'count': 1}),\n",
       "             ('surprising', {'value': 1.1, 'count': 1}),\n",
       "             ('dishonest', {'value': -2.7, 'count': 1}),\n",
       "             ('struck', {'value': -1.0, 'count': 1}),\n",
       "             ('ignoring', {'value': -1.7, 'count': 1}),\n",
       "             ('original', {'value': 1.3, 'count': 1}),\n",
       "             ('restless', {'value': -1.1, 'count': 1}),\n",
       "             ('pleasant', {'value': 2.3, 'count': 1}),\n",
       "             ('succession', {'value': 0.8, 'count': 1}),\n",
       "             ('longing', {'value': -0.1, 'count': 1}),\n",
       "             ('doomsday', {'value': -2.8, 'count': 1}),\n",
       "             ('applauds', {'value': 1.4, 'count': 1}),\n",
       "             ('awkward', {'value': -0.6, 'count': 1}),\n",
       "             ('perfecto', {'value': 1.3, 'count': 1}),\n",
       "             ('questioning', {'value': -0.4, 'count': 1}),\n",
       "             ('fag', {'value': -2.1, 'count': 1}),\n",
       "             ('happier', {'value': 2.4, 'count': 1}),\n",
       "             ('cancelled', {'value': -1.0, 'count': 1}),\n",
       "             ('daredevil', {'value': 0.5, 'count': 1}),\n",
       "             ('haunt', {'value': -1.7, 'count': 1}),\n",
       "             ('mourning', {'value': -1.9, 'count': 1}),\n",
       "             ('devastating', {'value': -3.3, 'count': 1}),\n",
       "             ('argue', {'value': -1.4, 'count': 1}),\n",
       "             ('wickedness', {'value': -2.1, 'count': 1}),\n",
       "             ('damnation', {'value': -2.6, 'count': 1}),\n",
       "             ('contradicts', {'value': -1.4, 'count': 1}),\n",
       "             ('harmlessness', {'value': 0.8, 'count': 1}),\n",
       "             ('sabotage', {'value': -2.4, 'count': 1}),\n",
       "             ('zzz', {'value': -1.2, 'count': 1}),\n",
       "             ('delight', {'value': 2.9, 'count': 1}),\n",
       "             ('doubter', {'value': -1.6, 'count': 1}),\n",
       "             ('demanding', {'value': -0.9, 'count': 1}),\n",
       "             ('enjoying', {'value': 2.4, 'count': 1}),\n",
       "             ('shake', {'value': -0.7, 'count': 1}),\n",
       "             ('reassurance', {'value': 1.5, 'count': 1}),\n",
       "             ('enthuse', {'value': 1.6, 'count': 1}),\n",
       "             ('outreach', {'value': 1.1, 'count': 1}),\n",
       "             ('livid', {'value': -2.5, 'count': 1}),\n",
       "             ('kisser', {'value': 1.7, 'count': 1}),\n",
       "             ('improvement', {'value': 2.0, 'count': 1}),\n",
       "             ('cruel', {'value': -2.8, 'count': 1}),\n",
       "             ('tumor', {'value': -1.6, 'count': 1}),\n",
       "             ('lowest', {'value': -1.6, 'count': 1}),\n",
       "             ('raging', {'value': -2.4, 'count': 1}),\n",
       "             ('foolishness', {'value': -1.8, 'count': 1}),\n",
       "             ('brightens', {'value': 1.5, 'count': 1}),\n",
       "             ('sweetness', {'value': 2.2, 'count': 1}),\n",
       "             ('crappy', {'value': -2.6, 'count': 1}),\n",
       "             ('dilemma', {'value': -0.7, 'count': 1}),\n",
       "             ('criticise', {'value': -1.9, 'count': 1}),\n",
       "             ('riot', {'value': -2.6, 'count': 1}),\n",
       "             ('threaten', {'value': -1.6, 'count': 1}),\n",
       "             ('disagrees', {'value': -1.3, 'count': 1}),\n",
       "             ('expose', {'value': -0.6, 'count': 1}),\n",
       "             ('adverse', {'value': -1.5, 'count': 1}),\n",
       "             ('illiteracy', {'value': -1.9, 'count': 1}),\n",
       "             ('solves', {'value': 1.1, 'count': 1}),\n",
       "             ('lament', {'value': -2.0, 'count': 1}),\n",
       "             ('undermines', {'value': -1.4, 'count': 1}),\n",
       "             ('indifference', {'value': -0.2, 'count': 1}),\n",
       "             ('inspire', {'value': 2.7, 'count': 1}),\n",
       "             ('graveyard', {'value': -1.2, 'count': 1}),\n",
       "             ('violates', {'value': -2.3, 'count': 1}),\n",
       "             ('bastard', {'value': -2.5, 'count': 1}),\n",
       "             ...])"
      ]
     },
     "execution_count": 192,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vader_dict4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 204,
   "id": "c6a2a5d6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "pos_val is [('one', 'CD'), ('want', 'NN'), ('disgruntled', 'VBD'), ('employee', 'NN'), ('pfizerbiontech', 'NN'), ('right', 'NN')]\n",
      "senti_val is [[], [0.0, 0.25, 0.75], [], [0.0, 0.0, 1.0], [], [0.0, 0.0, 1.0]]\n"
     ]
    }
   ],
   "source": [
    "from nltk.corpus import wordnet as wn\n",
    "from nltk.corpus import sentiwordnet as swn\n",
    "from nltk.stem import PorterStemmer\n",
    "from nltk.stem import WordNetLemmatizer\n",
    "lemmatizer = WordNetLemmatizer()\n",
    "\n",
    "def penn_to_wn(tag):\n",
    "    \"\"\"\n",
    "    Convert between the PennTreebank tags to simple Wordnet tags\n",
    "    \"\"\"\n",
    "    if tag.startswith('J'):\n",
    "        return wn.ADJ\n",
    "    elif tag.startswith('N'):\n",
    "        return wn.NOUN\n",
    "    elif tag.startswith('R'):\n",
    "        return wn.ADV\n",
    "    elif tag.startswith('V'):\n",
    "        return wn.VERB\n",
    "    return None\n",
    "\n",
    "def get_sentiment(word,tag):\n",
    "    \"\"\" returns list of pos neg and objective score. But returns empty list if not present in senti wordnet. \"\"\"\n",
    "\n",
    "    wn_tag = penn_to_wn(tag)\n",
    "    if wn_tag not in (wn.NOUN, wn.ADJ, wn.ADV):\n",
    "        return []\n",
    "\n",
    "    lemma = lemmatizer.lemmatize(word, pos=wn_tag)\n",
    "    if not lemma:\n",
    "        return []\n",
    "\n",
    "    synsets = wn.synsets(word, pos=wn_tag)\n",
    "    if not synsets:\n",
    "        return []\n",
    "\n",
    "    # Take the first sense, the most common\n",
    "    synset = synsets[0]\n",
    "    swn_synset = swn.senti_synset(synset.name())\n",
    "\n",
    "    return [swn_synset.pos_score(),swn_synset.neg_score(),swn_synset.obj_score()]\n",
    "\n",
    "for i in range(len(df4[:1])):\n",
    "    sentence = df4['nn_list'][i]\n",
    "    pos_tag = df4['pos'][i]\n",
    "    senti_val = [get_sentiment(x,y) for (x,y) in pos_tag]\n",
    "    print(f\"pos_val is {pos_tag}\")\n",
    "    print(f\"senti_val is {senti_val}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 216,
   "id": "2466405b",
   "metadata": {},
   "outputs": [],
   "source": [
    "sentiwordnet_dict1 = dict()\n",
    "sentiwordnet_dict2 = dict()\n",
    "sentiwordnet_dict3 = dict()\n",
    "sentiwordnet_dict4 = dict()\n",
    "\n",
    "for arr, df in [[sentiwordnet_dict1, df1], [sentiwordnet_dict2, df2], [sentiwordnet_dict3, df3], [sentiwordnet_dict4, df4]]:\n",
    "    for i in range(len(df)):\n",
    "        sentence = df['nn_list'][i]\n",
    "        pos_tag = df['pos'][i]\n",
    "        senti_val = [get_sentiment(x,y) for (x,y) in pos_tag]\n",
    "        counter = 0\n",
    "        for j in senti_val:\n",
    "            word = pos_tag[counter][0]\n",
    "            if j:\n",
    "                if word in arr:\n",
    "                    arr[word]['count']+=1\n",
    "                else:\n",
    "                    arr[word] = {'positive':j[0], 'negative':j[1], 'count':1}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 210,
   "id": "8712b801",
   "metadata": {},
   "outputs": [],
   "source": [
    "sentiwordnet_dict1 = OrderedDict(sorted(sentiwordnet_dict1.items(), reverse=True,\n",
    "       key = lambda x: getitem(x[1], 'count')))\n",
    "sentiwordnet_dict2 = OrderedDict(sorted(sentiwordnet_dict2.items(), reverse=True,\n",
    "       key = lambda x: getitem(x[1], 'count')))\n",
    "sentiwordnet_dict3 = OrderedDict(sorted(sentiwordnet_dict3.items(), reverse=True,\n",
    "       key = lambda x: getitem(x[1], 'count')))\n",
    "sentiwordnet_dict4 = OrderedDict(sorted(sentiwordnet_dict4.items(), reverse=True,\n",
    "       key = lambda x: getitem(x[1], 'count')))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 215,
   "id": "c2e6108e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "OrderedDict([('pincode', {'positive': 0.0, 'negative': 0.0, 'count': 55343}),\n",
       "             ('urban', {'positive': 0.0, 'negative': 0.0, 'count': 11311}),\n",
       "             ('moderna', {'positive': 0.75, 'negative': 0.0, 'count': 7947}),\n",
       "             ('bengaluru',\n",
       "              {'positive': 0.5, 'negative': 0.125, 'count': 7324}),\n",
       "             ('covaxin', {'positive': 0.0, 'negative': 0.0, 'count': 6469}),\n",
       "             ('man', {'positive': 0.0, 'negative': 0.0, 'count': 5277}),\n",
       "             ('free', {'positive': 0.375, 'negative': 0.0, 'count': 3822}),\n",
       "             ('got', {'positive': 0.0, 'negative': 0.0, 'count': 3657}),\n",
       "             ('rural', {'positive': 0.0, 'negative': 0.0, 'count': 3452}),\n",
       "             ('vaccine', {'positive': 0.0, 'negative': 0.0, 'count': 3270}),\n",
       "             ('fda', {'positive': 0.0, 'negative': 0.0, 'count': 3183}),\n",
       "             ('ocgn', {'positive': 0.0, 'negative': 0.0, 'count': 2967}),\n",
       "             ('pfizer', {'positive': 0.0, 'negative': 0.0, 'count': 2109}),\n",
       "             ('india', {'positive': 0.0, 'negative': 0.0, 'count': 2081}),\n",
       "             ('japan', {'positive': 0.0, 'negative': 0.0, 'count': 1772}),\n",
       "             ('breaking', {'positive': 0.0, 'negative': 0.0, 'count': 1719}),\n",
       "             ('bharat', {'positive': 0.0, 'negative': 0.0, 'count': 1699}),\n",
       "             ('bharatbiotech',\n",
       "              {'positive': 0.25, 'negative': 0.5, 'count': 1511}),\n",
       "             ('finally', {'positive': 0.0, 'negative': 0.0, 'count': 1387}),\n",
       "             ('drtedros', {'positive': 0.0, 'negative': 0.0, 'count': 1375}),\n",
       "             ('mrna', {'positive': 0.0, 'negative': 0.0, 'count': 1192}),\n",
       "             ('good', {'positive': 0.75, 'negative': 0.0, 'count': 1085}),\n",
       "             ('today', {'positive': 0.125, 'negative': 0.0, 'count': 1070}),\n",
       "             ('booster', {'positive': 0.0, 'negative': 0.0, 'count': 1064}),\n",
       "             ('vaccination',\n",
       "              {'positive': 0.375, 'negative': 0.25, 'count': 1017}),\n",
       "             ('fully', {'positive': 0.0, 'negative': 0.0, 'count': 1007}),\n",
       "             ('situation', {'positive': 0.0, 'negative': 0.0, 'count': 999}),\n",
       "             ('world', {'positive': 0.0, 'negative': 0.0, 'count': 974}),\n",
       "             ('first', {'positive': 0.0, 'negative': 0.0, 'count': 972}),\n",
       "             ('congratulation',\n",
       "              {'positive': 0.0, 'negative': 0.0, 'count': 915}),\n",
       "             ('sweden', {'positive': 0.0, 'negative': 0.0, 'count': 880}),\n",
       "             ('dose', {'positive': 0.0, 'negative': 0.0, 'count': 872}),\n",
       "             ('china', {'positive': 0.0, 'negative': 0.0, 'count': 815}),\n",
       "             ('great', {'positive': 0.0, 'negative': 0.0, 'count': 759}),\n",
       "             ('govcanhealth',\n",
       "              {'positive': 0.0, 'negative': 0.0, 'count': 757}),\n",
       "             ('cdc', {'positive': 0.0, 'negative': 0.0, 'count': 740}),\n",
       "             ('sinopharm', {'positive': 0.0, 'negative': 0.0, 'count': 733}),\n",
       "             ('big', {'positive': 0.25, 'negative': 0.125, 'count': 730}),\n",
       "             ('need', {'positive': 0.125, 'negative': 0.125, 'count': 729}),\n",
       "             ('potus', {'positive': 0.0, 'negative': 0.0, 'count': 712}),\n",
       "             ('one', {'positive': 0.0, 'negative': 0.25, 'count': 710}),\n",
       "             ('people', {'positive': 0.0, 'negative': 0.0, 'count': 691}),\n",
       "             ('get', {'positive': 0.0, 'negative': 0.0, 'count': 686}),\n",
       "             ('sputnikvaccine',\n",
       "              {'positive': 0.0, 'negative': 0.125, 'count': 646}),\n",
       "             ('sinovac', {'positive': 0.0, 'negative': 0.0, 'count': 641}),\n",
       "             ('health', {'positive': 0.625, 'negative': 0.0, 'count': 599}),\n",
       "             ('sputnikv', {'positive': 0.0, 'negative': 0.125, 'count': 599}),\n",
       "             ('doctorsoumya',\n",
       "              {'positive': 0.0, 'negative': 0.0, 'count': 589}),\n",
       "             ('indian', {'positive': 0.0, 'negative': 0.0, 'count': 579}),\n",
       "             ('another', {'positive': 0.5, 'negative': 0.0, 'count': 576}),\n",
       "             ('study', {'positive': 0.125, 'negative': 0.125, 'count': 573}),\n",
       "             ('two', {'positive': 0.0, 'negative': 0.0, 'count': 571}),\n",
       "             ('number', {'positive': 0.0, 'negative': 0.375, 'count': 556}),\n",
       "             ('subject', {'positive': 0.0, 'negative': 0.125, 'count': 554}),\n",
       "             ('approval', {'positive': 0.0, 'negative': 0.0, 'count': 549}),\n",
       "             ('cdcgov', {'positive': 0.0, 'negative': 0.0, 'count': 548}),\n",
       "             ('update', {'positive': 0.0, 'negative': 0.0, 'count': 543}),\n",
       "             ('anyone', {'positive': 0.0, 'negative': 0.0, 'count': 536}),\n",
       "             ('day', {'positive': 0.0, 'negative': 0.0, 'count': 515}),\n",
       "             ('chinese', {'positive': 0.0, 'negative': 0.0, 'count': 514}),\n",
       "             ('nihdirector', {'positive': 0.0, 'negative': 0.0, 'count': 509}),\n",
       "             ('canada', {'positive': 0.0, 'negative': 0.0, 'count': 486}),\n",
       "             ('expert', {'positive': 0.625, 'negative': 0.0, 'count': 458}),\n",
       "             ('narendramodi',\n",
       "              {'positive': 0.0, 'negative': 0.0, 'count': 458}),\n",
       "             ('ocugen', {'positive': 0.0, 'negative': 0.0, 'count': 450}),\n",
       "             ('repthomasmassie',\n",
       "              {'positive': 0.125, 'negative': 0.125, 'count': 447}),\n",
       "             ('many', {'positive': 0.0, 'negative': 0.0, 'count': 439}),\n",
       "             ('second', {'positive': 0.0, 'negative': 0.0, 'count': 434}),\n",
       "             ('well', {'positive': 0.375, 'negative': 0.0, 'count': 431}),\n",
       "             ('suchitraella',\n",
       "              {'positive': 0.0, 'negative': 0.0, 'count': 420}),\n",
       "             ('know', {'positive': 0.0, 'negative': 0.0, 'count': 412}),\n",
       "             ('ani', {'positive': 0.0, 'negative': 0.0, 'count': 408}),\n",
       "             ('million', {'positive': 0.0, 'negative': 0.0, 'count': 407}),\n",
       "             ('union', {'positive': 0.0, 'negative': 0.0, 'count': 400}),\n",
       "             ('still', {'positive': 0.0, 'negative': 0.125, 'count': 400}),\n",
       "             ('vaccinated', {'positive': 0.0, 'negative': 0.0, 'count': 399}),\n",
       "             ('government', {'positive': 0.0, 'negative': 0.0, 'count': 399}),\n",
       "             ('russia', {'positive': 0.0, 'negative': 0.0, 'count': 394}),\n",
       "             ('getting', {'positive': 0.875, 'negative': 0.0, 'count': 392}),\n",
       "             ('third', {'positive': 0.0, 'negative': 0.0, 'count': 379}),\n",
       "             ('dear', {'positive': 0.5, 'negative': 0.0, 'count': 376}),\n",
       "             ('data', {'positive': 0.0, 'negative': 0.0, 'count': 371}),\n",
       "             ('pfizerbiontech',\n",
       "              {'positive': 0.0, 'negative': 0.0, 'count': 368}),\n",
       "             ('thank', {'positive': 0.0, 'negative': 0.0, 'count': 364}),\n",
       "             ('time', {'positive': 0.0, 'negative': 0.0, 'count': 358}),\n",
       "             ('iceland', {'positive': 0.0, 'negative': 0.0, 'count': 354}),\n",
       "             ('hope', {'positive': 0.25, 'negative': 0.125, 'count': 352}),\n",
       "             ('look', {'positive': 0.0, 'negative': 0.0, 'count': 349}),\n",
       "             ('doe', {'positive': 0.0, 'negative': 0.0, 'count': 341}),\n",
       "             ('please', {'positive': 0.125, 'negative': 0.0, 'count': 340}),\n",
       "             ('justintrudeau',\n",
       "              {'positive': 0.125, 'negative': 0.0, 'count': 333}),\n",
       "             ('done', {'positive': 0.0, 'negative': 0.0, 'count': 329}),\n",
       "             ('news', {'positive': 0.0, 'negative': 0.0, 'count': 329}),\n",
       "             ('sputnik', {'positive': 0.0, 'negative': 0.0, 'count': 325}),\n",
       "             ('finland', {'positive': 0.0, 'negative': 0.0, 'count': 322}),\n",
       "             ('country', {'positive': 0.0, 'negative': 0.0, 'count': 320}),\n",
       "             ('dos', {'positive': 0.0, 'negative': 0.0, 'count': 317}),\n",
       "             ('cpho', {'positive': 0.0, 'negative': 0.0, 'count': 313}),\n",
       "             ('took', {'positive': 0.0, 'negative': 0.0, 'count': 312}),\n",
       "             ('coronavirus', {'positive': 0.0, 'negative': 0.0, 'count': 306}),\n",
       "             ('think', {'positive': 0.0, 'negative': 0.0, 'count': 306}),\n",
       "             ('take', {'positive': 0.0, 'negative': 0.0, 'count': 304}),\n",
       "             ('food', {'positive': 0.0, 'negative': 0.0, 'count': 300}),\n",
       "             ('let', {'positive': 0.0, 'negative': 0.125, 'count': 296}),\n",
       "             ('embarrassing',\n",
       "              {'positive': 0.125, 'negative': 0.0, 'count': 296}),\n",
       "             ('thanks', {'positive': 0.125, 'negative': 0.0, 'count': 293}),\n",
       "             ('crore', {'positive': 0.0, 'negative': 0.0, 'count': 292}),\n",
       "             ('selling', {'positive': 0.0, 'negative': 0.0, 'count': 291}),\n",
       "             ('drericding', {'positive': 0.0, 'negative': 0.0, 'count': 288}),\n",
       "             ('mansukhmandviya',\n",
       "              {'positive': 0.0, 'negative': 0.0, 'count': 287}),\n",
       "             ('hey', {'positive': 0.0, 'negative': 0.0, 'count': 284}),\n",
       "             ('hour', {'positive': 0.0, 'negative': 0.0, 'count': 283}),\n",
       "             ('thailand', {'positive': 0.0, 'negative': 0.0, 'count': 282}),\n",
       "             ('want', {'positive': 0.875, 'negative': 0.0, 'count': 280}),\n",
       "             ('read', {'positive': 0.0, 'negative': 0.0, 'count': 279}),\n",
       "             ('received', {'positive': 0.0, 'negative': 0.0, 'count': 279}),\n",
       "             ('check', {'positive': 0.0, 'negative': 0.0, 'count': 278}),\n",
       "             ('watch', {'positive': 0.0, 'negative': 0.0, 'count': 277}),\n",
       "             ('emergency',\n",
       "              {'positive': 0.125, 'negative': 0.625, 'count': 274}),\n",
       "             ('last', {'positive': 0.0, 'negative': 0.25, 'count': 267}),\n",
       "             ('technical', {'positive': 0.125, 'negative': 0.0, 'count': 265}),\n",
       "             ('approved', {'positive': 0.0, 'negative': 0.0, 'count': 262}),\n",
       "             ('decision', {'positive': 0.0, 'negative': 0.0, 'count': 260}),\n",
       "             ('made', {'positive': 0.875, 'negative': 0.125, 'count': 259}),\n",
       "             ('according', {'positive': 0.0, 'negative': 0.0, 'count': 257}),\n",
       "             ('single', {'positive': 0.0, 'negative': 0.0, 'count': 257}),\n",
       "             ('japanese', {'positive': 0.0, 'negative': 0.0, 'count': 256}),\n",
       "             ('vaers', {'positive': 0.0, 'negative': 0.375, 'count': 254}),\n",
       "             ('russian', {'positive': 0.0, 'negative': 0.0, 'count': 254}),\n",
       "             ('best', {'positive': 0.75, 'negative': 0.0, 'count': 250}),\n",
       "             ('erictopol', {'positive': 0.0, 'negative': 0.0, 'count': 249}),\n",
       "             ('rwmalonemd', {'positive': 0.0, 'negative': 0.0, 'count': 248}),\n",
       "             ('boosted', {'positive': 0.0, 'negative': 0.75, 'count': 247}),\n",
       "             ('happy', {'positive': 0.875, 'negative': 0.0, 'count': 245}),\n",
       "             ('yes', {'positive': 0.875, 'negative': 0.0, 'count': 245}),\n",
       "             ('report', {'positive': 0.0, 'negative': 0.0, 'count': 243}),\n",
       "             ('total', {'positive': 0.0, 'negative': 0.0, 'count': 243}),\n",
       "             ('germany', {'positive': 0.0, 'negative': 0.0, 'count': 243}),\n",
       "             ('looking', {'positive': 0.5, 'negative': 0.0, 'count': 239}),\n",
       "             ('amp', {'positive': 0.0, 'negative': 0.0, 'count': 238}),\n",
       "             ('really', {'positive': 0.625, 'negative': 0.0, 'count': 237}),\n",
       "             ('top', {'positive': 0.0, 'negative': 0.0, 'count': 236}),\n",
       "             ('australia', {'positive': 0.0, 'negative': 0.0, 'count': 235}),\n",
       "             ('interesting', {'positive': 0.0, 'negative': 0.0, 'count': 231}),\n",
       "             ('ontario', {'positive': 0.125, 'negative': 0.0, 'count': 230}),\n",
       "             ('brazil', {'positive': 0.125, 'negative': 0.0, 'count': 226}),\n",
       "             ('wonder', {'positive': 0.75, 'negative': 0.0, 'count': 225}),\n",
       "             ('dist', {'positive': 0.0, 'negative': 0.0, 'count': 224}),\n",
       "             ('hello', {'positive': 0.0, 'negative': 0.0, 'count': 223}),\n",
       "             ('dcgi', {'positive': 0.0, 'negative': 0.0, 'count': 219}),\n",
       "             ('would', {'positive': 0.0, 'negative': 0.0, 'count': 218}),\n",
       "             ('daily', {'positive': 0.0, 'negative': 0.0, 'count': 217}),\n",
       "             ('nytimes', {'positive': 0.75, 'negative': 0.0, 'count': 213}),\n",
       "             ('drug', {'positive': 0.0, 'negative': 0.0, 'count': 213}),\n",
       "             ('approves', {'positive': 0.0, 'negative': 0.0, 'count': 211}),\n",
       "             ('even', {'positive': 0.125, 'negative': 0.0, 'count': 208}),\n",
       "             ('official', {'positive': 0.0, 'negative': 0.0, 'count': 208}),\n",
       "             ('mikalche', {'positive': 0.125, 'negative': 0.0, 'count': 207}),\n",
       "             ('proud', {'positive': 0.375, 'negative': 0.25, 'count': 206}),\n",
       "             ('europe', {'positive': 0.0, 'negative': 0.0, 'count': 205}),\n",
       "             ('bangladesh', {'positive': 0.0, 'negative': 0.0, 'count': 202}),\n",
       "             ('covishield', {'positive': 0.0, 'negative': 0.0, 'count': 202}),\n",
       "             ('mohfw', {'positive': 0.0, 'negative': 0.0, 'count': 201}),\n",
       "             ('say', {'positive': 0.125, 'negative': 0.0, 'count': 200}),\n",
       "             ('latest', {'positive': 0.125, 'negative': 0.0, 'count': 200}),\n",
       "             ('welcome', {'positive': 0.5, 'negative': 0.0, 'count': 200}),\n",
       "             ('reuters', {'positive': 0.0, 'negative': 0.0, 'count': 198}),\n",
       "             ('breakingnews',\n",
       "              {'positive': 0.0, 'negative': 0.0, 'count': 196}),\n",
       "             ('safe', {'positive': 0.375, 'negative': 0.375, 'count': 195}),\n",
       "             ('glad', {'positive': 0.0, 'negative': 0.0, 'count': 192}),\n",
       "             ('wow', {'positive': 0.25, 'negative': 0.375, 'count': 191}),\n",
       "             ('child', {'positive': 0.0, 'negative': 0.0, 'count': 189}),\n",
       "             ('cnn', {'positive': 0.0, 'negative': 0.0, 'count': 188}),\n",
       "             ('shot', {'positive': 0.0, 'negative': 0.0, 'count': 187}),\n",
       "             ('come', {'positive': 0.0, 'negative': 0.0, 'count': 186}),\n",
       "             ('philippine', {'positive': 0.0, 'negative': 0.0, 'count': 185}),\n",
       "             ('national', {'positive': 0.0, 'negative': 0.0, 'count': 184}),\n",
       "             ('marketwatch', {'positive': 0.0, 'negative': 0.0, 'count': 183}),\n",
       "             ('trump', {'positive': 0.0, 'negative': 0.0, 'count': 182}),\n",
       "             ('year', {'positive': 0.0, 'negative': 0.0, 'count': 181}),\n",
       "             ('cambodia', {'positive': 0.0, 'negative': 0.0, 'count': 180}),\n",
       "             ('usa', {'positive': 0.0, 'negative': 0.0, 'count': 179}),\n",
       "             ('real', {'positive': 0.0, 'negative': 0.0, 'count': 178}),\n",
       "             ('foreign', {'positive': 0.0, 'negative': 0.125, 'count': 178}),\n",
       "             ('pmoindia', {'positive': 0.375, 'negative': 0.25, 'count': 178}),\n",
       "             ('granted', {'positive': 0.125, 'negative': 0.625, 'count': 178}),\n",
       "             ('singapore', {'positive': 0.0, 'negative': 0.0, 'count': 177}),\n",
       "             ('wan', {'positive': 0.0, 'negative': 0.0, 'count': 176}),\n",
       "             ('ten', {'positive': 0.0, 'negative': 0.0, 'count': 176}),\n",
       "             ('cemi', {'positive': 0.0, 'negative': 0.0, 'count': 176}),\n",
       "             ('question', {'positive': 0.125, 'negative': 0.25, 'count': 174}),\n",
       "             ('nigeria', {'positive': 0.0, 'negative': 0.0, 'count': 174}),\n",
       "             ('govt', {'positive': 0.0, 'negative': 0.0, 'count': 173}),\n",
       "             ('week', {'positive': 0.0, 'negative': 0.0, 'count': 173}),\n",
       "             ('long', {'positive': 0.0, 'negative': 0.0, 'count': 171}),\n",
       "             ('video', {'positive': 0.0, 'negative': 0.0, 'count': 171}),\n",
       "             ('said', {'positive': 0.25, 'negative': 0.125, 'count': 170}),\n",
       "             ('important', {'positive': 0.875, 'negative': 0.0, 'count': 170}),\n",
       "             ('increase', {'positive': 0.0, 'negative': 0.0, 'count': 170}),\n",
       "             ('sep', {'positive': 0.0, 'negative': 0.0, 'count': 170}),\n",
       "             ('case', {'positive': 0.0, 'negative': 0.0, 'count': 169}),\n",
       "             ('cdcdirector', {'positive': 0.0, 'negative': 0.0, 'count': 168}),\n",
       "             ('live', {'positive': 0.0, 'negative': 0.0, 'count': 168}),\n",
       "             ('huge', {'positive': 0.0, 'negative': 0.125, 'count': 167}),\n",
       "             ('going', {'positive': 0.0, 'negative': 0.0, 'count': 167}),\n",
       "             ('make', {'positive': 0.375, 'negative': 0.0, 'count': 167}),\n",
       "             ('next', {'positive': 0.0, 'negative': 0.0, 'count': 165}),\n",
       "             ('also', {'positive': 0.0, 'negative': 0.0, 'count': 165}),\n",
       "             ('sec', {'positive': 0.0, 'negative': 0.0, 'count': 165}),\n",
       "             ('wait', {'positive': 0.0, 'negative': 0.0, 'count': 164}),\n",
       "             ('covidvaccine',\n",
       "              {'positive': 0.0, 'negative': 0.0, 'count': 163}),\n",
       "             ('biden', {'positive': 0.375, 'negative': 0.0, 'count': 162}),\n",
       "             ('full', {'positive': 0.0, 'negative': 0.0, 'count': 161}),\n",
       "             ('since', {'positive': 0.0, 'negative': 0.0, 'count': 160}),\n",
       "             ('give', {'positive': 0.125, 'negative': 0.0, 'count': 156}),\n",
       "             ('seems', {'positive': 0.0, 'negative': 0.0, 'count': 155}),\n",
       "             ('feeling', {'positive': 0.0, 'negative': 0.0, 'count': 154}),\n",
       "             ('south', {'positive': 0.0, 'negative': 0.0, 'count': 153}),\n",
       "             ('joebiden', {'positive': 0.0, 'negative': 0.0, 'count': 153}),\n",
       "             ('never', {'positive': 0.0, 'negative': 0.625, 'count': 151}),\n",
       "             ('waiting', {'positive': 0.0, 'negative': 0.0, 'count': 151}),\n",
       "             ('bahrain', {'positive': 0.0, 'negative': 0.0, 'count': 149}),\n",
       "             ('kenya', {'positive': 0.0, 'negative': 0.0, 'count': 148}),\n",
       "             ('french', {'positive': 0.0, 'negative': 0.0, 'count': 148}),\n",
       "             ('yesterday', {'positive': 0.0, 'negative': 0.0, 'count': 147}),\n",
       "             ('add', {'positive': 0.0, 'negative': 0.0, 'count': 147}),\n",
       "             ('much', {'positive': 0.0, 'negative': 0.0, 'count': 147}),\n",
       "             ('whosearo', {'positive': 0.0, 'negative': 0.0, 'count': 147}),\n",
       "             ('okay', {'positive': 0.375, 'negative': 0.0, 'count': 145}),\n",
       "             ('britain', {'positive': 0.0, 'negative': 0.0, 'count': 145}),\n",
       "             ('stock', {'positive': 0.0, 'negative': 0.0, 'count': 143}),\n",
       "             ('feel', {'positive': 0.125, 'negative': 0.0, 'count': 142}),\n",
       "             ('icymi', {'positive': 0.0, 'negative': 0.0, 'count': 142}),\n",
       "             ('delta', {'positive': 0.125, 'negative': 0.5, 'count': 141}),\n",
       "             ('egypt', {'positive': 0.125, 'negative': 0.0, 'count': 141}),\n",
       "             ('business', {'positive': 0.0, 'negative': 0.0, 'count': 140}),\n",
       "             ('celliottability',\n",
       "              {'positive': 0.125, 'negative': 0.0, 'count': 140}),\n",
       "             ('ktla', {'positive': 0.125, 'negative': 0.625, 'count': 140}),\n",
       "             ('side', {'positive': 0.0, 'negative': 0.0, 'count': 139}),\n",
       "             ('american', {'positive': 0.0, 'negative': 0.0, 'count': 139}),\n",
       "             ('drmikeryan', {'positive': 0.0, 'negative': 0.0, 'count': 139}),\n",
       "             ('everyone', {'positive': 0.375, 'negative': 0.0, 'count': 138}),\n",
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       "             ('perfect', {'positive': 0.625, 'negative': 0.125, 'count': 38}),\n",
       "             ('wonderful', {'positive': 0.75, 'negative': 0.0, 'count': 38}),\n",
       "             ('agency', {'positive': 0.0, 'negative': 0.0, 'count': 38}),\n",
       "             ('hearing', {'positive': 0.0, 'negative': 0.0, 'count': 38}),\n",
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       "              {'positive': 0.375, 'negative': 0.375, 'count': 38}),\n",
       "             ('ashishkjha', {'positive': 0.125, 'negative': 0.0, 'count': 38}),\n",
       "             ('mfa', {'positive': 0.0, 'negative': 0.0, 'count': 38}),\n",
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       "             ('lost', {'positive': 0.125, 'negative': 0.0, 'count': 38}),\n",
       "             ('poll', {'positive': 0.0, 'negative': 0.0, 'count': 38}),\n",
       "             ('denmark', {'positive': 0.0, 'negative': 0.0, 'count': 38}),\n",
       "             ('oral', {'positive': 0.0, 'negative': 0.0, 'count': 38}),\n",
       "             ('nswhealth', {'positive': 0.0, 'negative': 0.0, 'count': 37}),\n",
       "             ('medicine', {'positive': 0.0, 'negative': 0.0, 'count': 37}),\n",
       "             ('fun', {'positive': 0.375, 'negative': 0.0, 'count': 37}),\n",
       "             ('cnnbrk', {'positive': 0.0, 'negative': 0.0, 'count': 37}),\n",
       "             ('informed', {'positive': 0.0, 'negative': 0.0, 'count': 37}),\n",
       "             ('officialncoc', {'positive': 0.5, 'negative': 0.0, 'count': 37}),\n",
       "             ('covax', {'positive': 0.0, 'negative': 0.0, 'count': 37}),\n",
       "             ('rare', {'positive': 0.25, 'negative': 0.0, 'count': 37}),\n",
       "             ('pregnant', {'positive': 0.0, 'negative': 0.0, 'count': 37}),\n",
       "             ('absolutely', {'positive': 0.5, 'negative': 0.0, 'count': 37}),\n",
       "             ('surprise', {'positive': 0.375, 'negative': 0.25, 'count': 37}),\n",
       "             ('immunity', {'positive': 0.125, 'negative': 0.25, 'count': 37}),\n",
       "             ('patient', {'positive': 0.5, 'negative': 0.25, 'count': 37}),\n",
       "             ('reddy', {'positive': 0.0, 'negative': 0.0, 'count': 37}),\n",
       "             ('security', {'positive': 0.5, 'negative': 0.125, 'count': 37}),\n",
       "             ('secblinken', {'positive': 0.0, 'negative': 0.0, 'count': 37}),\n",
       "             ('accepts', {'positive': 0.125, 'negative': 0.625, 'count': 37}),\n",
       "             ('ridiculous', {'positive': 0.0, 'negative': 0.625, 'count': 36}),\n",
       "             ('receiving', {'positive': 0.0, 'negative': 0.0, 'count': 36}),\n",
       "             ('nbcnews', {'positive': 0.0, 'negative': 0.0, 'count': 36}),\n",
       "             ('happens', {'positive': 0.0, 'negative': 0.0, 'count': 36}),\n",
       "             ('bigpharma', {'positive': 0.0, 'negative': 0.0, 'count': 36}),\n",
       "             ('bnodesk', {'positive': 0.0, 'negative': 0.0, 'count': 36}),\n",
       "             ('set', {'positive': 0.125, 'negative': 0.25, 'count': 36}),\n",
       "             ('polio', {'positive': 0.0, 'negative': 0.625, 'count': 36}),\n",
       "             ('sajidjavid', {'positive': 0.0, 'negative': 0.0, 'count': 36}),\n",
       "             ('putin', {'positive': 0.0, 'negative': 0.0, 'count': 36}),\n",
       "             ('independent',\n",
       "              {'positive': 0.125, 'negative': 0.0, 'count': 36}),\n",
       "             ('spokespersonchn',\n",
       "              {'positive': 0.0, 'negative': 0.0, 'count': 35}),\n",
       "             ('therapeutic',\n",
       "              {'positive': 0.875, 'negative': 0.0, 'count': 35}),\n",
       "             ('senior', {'positive': 0.375, 'negative': 0.0, 'count': 35}),\n",
       "             ('person', {'positive': 0.0, 'negative': 0.0, 'count': 35}),\n",
       "             ('june', {'positive': 0.0, 'negative': 0.0, 'count': 35}),\n",
       "             ('ivermectin', {'positive': 0.75, 'negative': 0.0, 'count': 35}),\n",
       "             ('worse', {'positive': 0.0, 'negative': 0.75, 'count': 35}),\n",
       "             ('investigates',\n",
       "              {'positive': 0.25, 'negative': 0.0, 'count': 35}),\n",
       "             ('surgeon', {'positive': 0.0, 'negative': 0.0, 'count': 35}),\n",
       "             ('worried', {'positive': 0.0, 'negative': 0.875, 'count': 35}),\n",
       "             ('name', {'positive': 0.0, 'negative': 0.0, 'count': 35}),\n",
       "             ('saudiarabia', {'positive': 0.0, 'negative': 0.0, 'count': 35}),\n",
       "             ('ndtvexclusive',\n",
       "              {'positive': 0.0, 'negative': 0.0, 'count': 35}),\n",
       "             ('perhaps', {'positive': 0.0, 'negative': 0.0, 'count': 35}),\n",
       "             ('cuba', {'positive': 0.0, 'negative': 0.0, 'count': 35}),\n",
       "             ('cool', {'positive': 0.25, 'negative': 0.375, 'count': 35}),\n",
       "             ('wanted', {'positive': 0.0, 'negative': 0.0, 'count': 35}),\n",
       "             ('gurugram', {'positive': 0.0, 'negative': 0.0, 'count': 35}),\n",
       "             ('father', {'positive': 0.0, 'negative': 0.0, 'count': 35}),\n",
       "             ('mix', {'positive': 0.0, 'negative': 0.0, 'count': 35}),\n",
       "             ('lithuania', {'positive': 0.0, 'negative': 0.0, 'count': 35}),\n",
       "             ('told', {'positive': 0.0, 'negative': 0.0, 'count': 34}),\n",
       "             ('inactivated', {'positive': 0.0, 'negative': 0.0, 'count': 34}),\n",
       "             ('line', {'positive': 0.0, 'negative': 0.0, 'count': 34}),\n",
       "             ('access', {'positive': 0.0, 'negative': 0.0, 'count': 34}),\n",
       "             ('unless', {'positive': 0.0, 'negative': 0.0, 'count': 34}),\n",
       "             ('turn', {'positive': 0.0, 'negative': 0.0, 'count': 34}),\n",
       "             ('search', {'positive': 0.0, 'negative': 0.0, 'count': 34}),\n",
       "             ('highly', {'positive': 0.625, 'negative': 0.0, 'count': 34}),\n",
       "             ('developed', {'positive': 0.0, 'negative': 0.0, 'count': 34}),\n",
       "             ('pahowho', {'positive': 0.0, 'negative': 0.0, 'count': 34}),\n",
       "             ('btw', {'positive': 0.625, 'negative': 0.0, 'count': 34}),\n",
       "             ('spain', {'positive': 0.0, 'negative': 0.0, 'count': 34}),\n",
       "             ('google', {'positive': 0.0, 'negative': 0.0, 'count': 34}),\n",
       "             ('race', {'positive': 0.125, 'negative': 0.0, 'count': 34}),\n",
       "             ('sharing', {'positive': 0.0, 'negative': 0.0, 'count': 34}),\n",
       "             ('giant', {'positive': 0.375, 'negative': 0.0, 'count': 34}),\n",
       "             ('quote', {'positive': 0.0, 'negative': 0.0, 'count': 34}),\n",
       "             ('globaltimesnews',\n",
       "              {'positive': 0.125, 'negative': 0.0, 'count': 34}),\n",
       "             ('booked', {'positive': 0.375, 'negative': 0.0, 'count': 34}),\n",
       "             ('stage', {'positive': 0.0, 'negative': 0.0, 'count': 34}),\n",
       "             ('italy', {'positive': 0.0, 'negative': 0.0, 'count': 34}),\n",
       "             ('kishankaru', {'positive': 0.0, 'negative': 0.0, 'count': 33}),\n",
       "             ('five', {'positive': 0.0, 'negative': 0.0, 'count': 33}),\n",
       "             ('hmm', {'positive': 0.25, 'negative': 0.5, 'count': 33}),\n",
       "             ('something', {'positive': 0.0, 'negative': 0.5, 'count': 33}),\n",
       "             ('celinegounder',\n",
       "              {'positive': 0.125, 'negative': 0.25, 'count': 33}),\n",
       "             ('brilliant', {'positive': 0.875, 'negative': 0.0, 'count': 33}),\n",
       "             ('rockefeller', {'positive': 0.0, 'negative': 0.0, 'count': 33}),\n",
       "             ('damn', {'positive': 0.125, 'negative': 0.125, 'count': 33}),\n",
       "             ('mid', {'positive': 0.0, 'negative': 0.0, 'count': 33}),\n",
       "             ('crazy', {'positive': 0.0, 'negative': 0.5, 'count': 33}),\n",
       "             ('chennaicorp', {'positive': 0.0, 'negative': 0.0, 'count': 33}),\n",
       "             ('special', {'positive': 0.0, 'negative': 0.0, 'count': 33}),\n",
       "             ('nidhi', {'positive': 0.0, 'negative': 0.0, 'count': 33}),\n",
       "             ('closed', {'positive': 0.0, 'negative': 0.5, 'count': 33}),\n",
       "             ('strange', {'positive': 0.375, 'negative': 0.125, 'count': 33}),\n",
       "             ('tried', {'positive': 0.625, 'negative': 0.0, 'count': 32}),\n",
       "             ('logo', {'positive': 0.0, 'negative': 0.0, 'count': 32}),\n",
       "             ('lucky', {'positive': 0.75, 'negative': 0.0, 'count': 32}),\n",
       "             ('dgci', {'positive': 0.0, 'negative': 0.0, 'count': 32}),\n",
       "             ('brother', {'positive': 0.0, 'negative': 0.0, 'count': 32}),\n",
       "             ('lockdown', {'positive': 0.0, 'negative': 0.0, 'count': 32}),\n",
       "             ('higher', {'positive': 0.5, 'negative': 0.125, 'count': 32}),\n",
       "             ('harvard', {'positive': 0.0, 'negative': 0.0, 'count': 32}),\n",
       "             ('oregon', {'positive': 0.0, 'negative': 0.0, 'count': 32}),\n",
       "             ('washington', {'positive': 0.0, 'negative': 0.0, 'count': 32}),\n",
       "             ('age', {'positive': 0.0, 'negative': 0.0, 'count': 32}),\n",
       "             ('citizen', {'positive': 0.0, 'negative': 0.0, 'count': 32}),\n",
       "             ('thousand', {'positive': 0.0, 'negative': 0.0, 'count': 32}),\n",
       "             ('enter', {'positive': 0.0, 'negative': 0.0, 'count': 32}),\n",
       "             ('left', {'positive': 0.0, 'negative': 0.0, 'count': 32}),\n",
       "             ('yup', {'positive': 0.0, 'negative': 0.0, 'count': 32}),\n",
       "             ('drsimonegold', {'positive': 0.0, 'negative': 0.0, 'count': 32}),\n",
       "             ('ever', {'positive': 0.0, 'negative': 0.0, 'count': 32}),\n",
       "             ('evening', {'positive': 0.0, 'negative': 0.0, 'count': 32}),\n",
       "             ('idle', {'positive': 0.0, 'negative': 0.625, 'count': 32}),\n",
       "             ('approving', {'positive': 0.0, 'negative': 0.0, 'count': 32}),\n",
       "             ('game', {'positive': 0.0, 'negative': 0.0, 'count': 32}),\n",
       "             ('httweets', {'positive': 0.0, 'negative': 0.0, 'count': 32}),\n",
       "             ('marathon', {'positive': 0.0, 'negative': 0.0, 'count': 32}),\n",
       "             ('alexwellis', {'positive': 0.0, 'negative': 0.25, 'count': 32}),\n",
       "             ('lifted', {'positive': 0.0, 'negative': 0.0, 'count': 32}),\n",
       "             ('momentous', {'positive': 0.25, 'negative': 0.0, 'count': 32}),\n",
       "             ('ajitdatta', {'positive': 0.375, 'negative': 0.0, 'count': 32}),\n",
       "             ('cambodian', {'positive': 0.0, 'negative': 0.0, 'count': 32}),\n",
       "             ('wednesdaysnyder',\n",
       "              {'positive': 0.0, 'negative': 0.25, 'count': 32}),\n",
       "             ('chart', {'positive': 0.0, 'negative': 0.0, 'count': 31}),\n",
       "             ('novavax', {'positive': 0.0, 'negative': 0.0, 'count': 31}),\n",
       "             ('faheemyounus', {'positive': 0.5, 'negative': 0.0, 'count': 31}),\n",
       "             ('benefit', {'positive': 0.0, 'negative': 0.0, 'count': 31}),\n",
       "             ('criminal', {'positive': 0.0, 'negative': 0.875, 'count': 31}),\n",
       "             ('successfully',\n",
       "              {'positive': 0.125, 'negative': 0.0, 'count': 31}),\n",
       "             ('worry', {'positive': 0.25, 'negative': 0.0, 'count': 31}),\n",
       "             ('finding', {'positive': 0.0, 'negative': 0.0, 'count': 31}),\n",
       "             ('order', {'positive': 0.0, 'negative': 0.0, 'count': 31}),\n",
       "             ('alright', {'positive': 0.0, 'negative': 0.0, 'count': 31}),\n",
       "             ('probably', {'positive': 0.0, 'negative': 0.0, 'count': 31}),\n",
       "             ('pharmacist', {'positive': 0.125, 'negative': 0.0, 'count': 31}),\n",
       "             ('faq', {'positive': 0.0, 'negative': 0.0, 'count': 31}),\n",
       "             ('waited', {'positive': 0.0, 'negative': 0.0, 'count': 31}),\n",
       "             ('combat', {'positive': 0.0, 'negative': 0.0, 'count': 31}),\n",
       "             ('danielandrewsmp',\n",
       "              {'positive': 0.0, 'negative': 0.0, 'count': 31}),\n",
       "             ('nasal', {'positive': 0.0, 'negative': 0.0, 'count': 31}),\n",
       "             ('pretty', {'positive': 0.125, 'negative': 0.25, 'count': 31}),\n",
       "             ('court', {'positive': 0.0, 'negative': 0.0, 'count': 31}),\n",
       "             ('trend', {'positive': 0.0, 'negative': 0.0, 'count': 31}),\n",
       "             ('drkrishnaella',\n",
       "              {'positive': 0.0, 'negative': 0.0, 'count': 31}),\n",
       "             ('adult', {'positive': 0.0, 'negative': 0.0, 'count': 31}),\n",
       "             ('resident', {'positive': 0.0, 'negative': 0.0, 'count': 31}),\n",
       "             ('started', {'positive': 0.0, 'negative': 0.0, 'count': 31}),\n",
       "             ('icmrdelhi', {'positive': 0.0, 'negative': 0.0, 'count': 31}),\n",
       "             ('newsmax', {'positive': 0.0, 'negative': 0.0, 'count': 31}),\n",
       "             ('clearance', {'positive': 0.0, 'negative': 0.0, 'count': 31}),\n",
       "             ('process', {'positive': 0.0, 'negative': 0.0, 'count': 31}),\n",
       "             ('tga', {'positive': 0.0, 'negative': 0.0, 'count': 31}),\n",
       "             ('mailonline', {'positive': 0.125, 'negative': 0.0, 'count': 31}),\n",
       "             ('clearly', {'positive': 0.25, 'negative': 0.25, 'count': 31}),\n",
       "             ('serbia', {'positive': 0.0, 'negative': 0.0, 'count': 31}),\n",
       "             ('percent', {'positive': 0.0, 'negative': 0.0, 'count': 31}),\n",
       "             ('episode', {'positive': 0.0, 'negative': 0.0, 'count': 31}),\n",
       "             ('interested', {'positive': 0.625, 'negative': 0.0, 'count': 31}),\n",
       "             ('shekhargupta',\n",
       "              {'positive': 0.125, 'negative': 0.25, 'count': 31}),\n",
       "             ('maggie', {'positive': 0.0, 'negative': 0.0, 'count': 31}),\n",
       "             ('politician', {'positive': 0.0, 'negative': 0.0, 'count': 31}),\n",
       "             ('vaccinating', {'positive': 0.0, 'negative': 0.0, 'count': 31}),\n",
       "             ('davidmweissman',\n",
       "              {'positive': 0.0, 'negative': 0.0, 'count': 31}),\n",
       "             ('change', {'positive': 0.0, 'negative': 0.0, 'count': 31}),\n",
       "             ('topheadlines', {'positive': 0.0, 'negative': 0.0, 'count': 31}),\n",
       "             ('spreading', {'positive': 0.0, 'negative': 0.0, 'count': 31}),\n",
       "             ('nepal', {'positive': 0.0, 'negative': 0.0, 'count': 30}),\n",
       "             ('failure', {'positive': 0.0, 'negative': 0.0, 'count': 30}),\n",
       "             ('venezuela', {'positive': 0.0, 'negative': 0.0, 'count': 30}),\n",
       "             ('allow', {'positive': 0.0, 'negative': 0.0, 'count': 30}),\n",
       "             ('drugmaker', {'positive': 0.0, 'negative': 0.0, 'count': 30}),\n",
       "             ('appointment', {'positive': 0.0, 'negative': 0.0, 'count': 30}),\n",
       "             ('demand', {'positive': 0.0, 'negative': 0.25, 'count': 30}),\n",
       "             ('backed', {'positive': 0.0, 'negative': 0.0, 'count': 30}),\n",
       "             ('register', {'positive': 0.0, 'negative': 0.0, 'count': 30}),\n",
       "             ('link', {'positive': 0.0, 'negative': 0.0, 'count': 30}),\n",
       "             ('small', {'positive': 0.0, 'negative': 0.375, 'count': 30}),\n",
       "             ('contamination',\n",
       "              {'positive': 0.0, 'negative': 0.0, 'count': 30}),\n",
       "             ('proof', {'positive': 0.0, 'negative': 0.0, 'count': 30}),\n",
       "             ('ambassador', {'positive': 0.375, 'negative': 0.0, 'count': 30}),\n",
       "             ('become', {'positive': 0.625, 'negative': 0.0, 'count': 30}),\n",
       "             ('status', {'positive': 0.0, 'negative': 0.0, 'count': 30}),\n",
       "             ('saying', {'positive': 0.0, 'negative': 0.0, 'count': 30}),\n",
       "             ('injection', {'positive': 0.0, 'negative': 0.0, 'count': 30}),\n",
       "             ('chetan', {'positive': 0.0, 'negative': 0.0, 'count': 30}),\n",
       "             ('globeandmail',\n",
       "              {'positive': 0.125, 'negative': 0.0, 'count': 30}),\n",
       "             ('investor', {'positive': 0.0, 'negative': 0.0, 'count': 30}),\n",
       "             ('discrimination',\n",
       "              {'positive': 0.0, 'negative': 0.0, 'count': 30}),\n",
       "             ('axios', {'positive': 0.5, 'negative': 0.375, 'count': 30}),\n",
       "             ('aiims', {'positive': 0.0, 'negative': 0.0, 'count': 30}),\n",
       "             ('armenia', {'positive': 0.375, 'negative': 0.625, 'count': 30}),\n",
       "             ('reutersfacts',\n",
       "              {'positive': 0.375, 'negative': 0.0, 'count': 30}),\n",
       "             ('tvmohandaspai',\n",
       "              {'positive': 0.0, 'negative': 0.0, 'count': 30}),\n",
       "             ('trendingtonight',\n",
       "              {'positive': 0.25, 'negative': 0.0, 'count': 30}),\n",
       "             ...])"
      ]
     },
     "execution_count": 215,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sentiwordnet_dict4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0616bf1c",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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