Sentiment Per Review Group By Segment.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Import Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>recommendationid</th>\n",
       "      <th>language</th>\n",
       "      <th>review</th>\n",
       "      <th>timestamp_created</th>\n",
       "      <th>timestamp_updated</th>\n",
       "      <th>voted_up</th>\n",
       "      <th>votes_up</th>\n",
       "      <th>votes_funny</th>\n",
       "      <th>weighted_vote_score</th>\n",
       "      <th>comment_count</th>\n",
       "      <th>...</th>\n",
       "      <th>steamid</th>\n",
       "      <th>num_games_owned</th>\n",
       "      <th>num_reviews</th>\n",
       "      <th>playtime_forever</th>\n",
       "      <th>playtime_last_two_weeks</th>\n",
       "      <th>playtime_at_review</th>\n",
       "      <th>last_played</th>\n",
       "      <th>attributes_patterns</th>\n",
       "      <th>POS_all</th>\n",
       "      <th>cluster</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>157603005</td>\n",
       "      <td>english</td>\n",
       "      <td>fun game to play when you've had a long day an...</td>\n",
       "      <td>1707077354</td>\n",
       "      <td>1707077354</td>\n",
       "      <td>True</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>76561199401546423</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2027</td>\n",
       "      <td>322</td>\n",
       "      <td>1986</td>\n",
       "      <td>1707079884</td>\n",
       "      <td>['long day', 'fun easy', 'easy game']</td>\n",
       "      <td>['fun', 'game', 'play', \"'ve\", 'had', 'long', ...</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>157602265</td>\n",
       "      <td>english</td>\n",
       "      <td>pros:\\nanimals\\nparty\\nfunny mechanics\\na bunc...</td>\n",
       "      <td>1707076676</td>\n",
       "      <td>1707076676</td>\n",
       "      <td>True</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>76561198056822321</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>316</td>\n",
       "      <td>316</td>\n",
       "      <td>316</td>\n",
       "      <td>1707076353</td>\n",
       "      <td>['party funny', 'funny mechanics', 'short whil...</td>\n",
       "      <td>['pros', 'animals', 'party', 'funny', 'mechani...</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>157598111</td>\n",
       "      <td>english</td>\n",
       "      <td>10/10 would drop kick a cat without feeling bad</td>\n",
       "      <td>1707072837</td>\n",
       "      <td>1707072837</td>\n",
       "      <td>True</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>76561198428748427</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>847</td>\n",
       "      <td>410</td>\n",
       "      <td>804</td>\n",
       "      <td>1707075660</td>\n",
       "      <td>[]</td>\n",
       "      <td>['drop', 'kick', 'cat', 'feeling', 'bad']</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>157592673</td>\n",
       "      <td>english</td>\n",
       "      <td>very fun game. the animals are very cute, the ...</td>\n",
       "      <td>1707068019</td>\n",
       "      <td>1707068019</td>\n",
       "      <td>True</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>76561197987140501</td>\n",
       "      <td>0</td>\n",
       "      <td>10</td>\n",
       "      <td>7864</td>\n",
       "      <td>600</td>\n",
       "      <td>7712</td>\n",
       "      <td>1707109536</td>\n",
       "      <td>['fun game', 'new skins/characters']</td>\n",
       "      <td>['very', 'fun', 'game', 'animals', 'are', 'ver...</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>157588222</td>\n",
       "      <td>english</td>\n",
       "      <td>game has been out for 4 months and they only a...</td>\n",
       "      <td>1707064205</td>\n",
       "      <td>1707064205</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>76561198024864749</td>\n",
       "      <td>375</td>\n",
       "      <td>17</td>\n",
       "      <td>1456</td>\n",
       "      <td>0</td>\n",
       "      <td>1456</td>\n",
       "      <td>1701029993</td>\n",
       "      <td>['new content', 'old level', 'social hub']</td>\n",
       "      <td>['game', 'has', 'been', 'months', 'only', 'add...</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>157571234</td>\n",
       "      <td>english</td>\n",
       "      <td>i wanted a silly game, i got that.\\ni wanted s...</td>\n",
       "      <td>1707049256</td>\n",
       "      <td>1707049256</td>\n",
       "      <td>True</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>76561198186335569</td>\n",
       "      <td>85</td>\n",
       "      <td>31</td>\n",
       "      <td>77</td>\n",
       "      <td>77</td>\n",
       "      <td>46</td>\n",
       "      <td>1707116433</td>\n",
       "      <td>['silly game', 'non gamers', 'free pass']</td>\n",
       "      <td>['i', 'wanted', 'silly', 'game', 'i', 'got', '...</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>157560216</td>\n",
       "      <td>english</td>\n",
       "      <td>amazing!!! at first i could not punch but i ev...</td>\n",
       "      <td>1707038018</td>\n",
       "      <td>1707038018</td>\n",
       "      <td>True</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>76561199554790429</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>302</td>\n",
       "      <td>302</td>\n",
       "      <td>302</td>\n",
       "      <td>1707007872</td>\n",
       "      <td>['first i', 'same problem']</td>\n",
       "      <td>['amazing', 'first', 'i', 'not', 'punch', 'i',...</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>157554594</td>\n",
       "      <td>english</td>\n",
       "      <td>it's just super fun. i love pvpin friends</td>\n",
       "      <td>1707030713</td>\n",
       "      <td>1707030713</td>\n",
       "      <td>True</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>76561199503158814</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>3482</td>\n",
       "      <td>1295</td>\n",
       "      <td>3316</td>\n",
       "      <td>1707116694</td>\n",
       "      <td>['super fun']</td>\n",
       "      <td>[\"'s\", 'just', 'super', 'fun', 'i', 'love', 'p...</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>157545950</td>\n",
       "      <td>english</td>\n",
       "      <td>it is so fun to play with friends and even wit...</td>\n",
       "      <td>1707019575</td>\n",
       "      <td>1707019575</td>\n",
       "      <td>True</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>76561199616500100</td>\n",
       "      <td>11</td>\n",
       "      <td>1</td>\n",
       "      <td>358</td>\n",
       "      <td>358</td>\n",
       "      <td>318</td>\n",
       "      <td>1707081153</td>\n",
       "      <td>[]</td>\n",
       "      <td>['is', 'so', 'fun', 'play', 'friends', 'even',...</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>157539476</td>\n",
       "      <td>english</td>\n",
       "      <td>good game, despite all the controversy</td>\n",
       "      <td>1707011714</td>\n",
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       "      <td>True</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>76561197984794794</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>423</td>\n",
       "      <td>21</td>\n",
       "      <td>423</td>\n",
       "      <td>1707011693</td>\n",
       "      <td>['good game']</td>\n",
       "      <td>['good', 'game', 'controversy']</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>10 rows × 27 columns</p>\n",
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      ],
      "text/plain": [
       "   recommendationid language  \\\n",
       "0         157603005  english   \n",
       "1         157602265  english   \n",
       "2         157598111  english   \n",
       "3         157592673  english   \n",
       "4         157588222  english   \n",
       "5         157571234  english   \n",
       "6         157560216  english   \n",
       "7         157554594  english   \n",
       "8         157545950  english   \n",
       "9         157539476  english   \n",
       "\n",
       "                                              review  timestamp_created  \\\n",
       "0  fun game to play when you've had a long day an...         1707077354   \n",
       "1  pros:\\nanimals\\nparty\\nfunny mechanics\\na bunc...         1707076676   \n",
       "2    10/10 would drop kick a cat without feeling bad         1707072837   \n",
       "3  very fun game. the animals are very cute, the ...         1707068019   \n",
       "4  game has been out for 4 months and they only a...         1707064205   \n",
       "5  i wanted a silly game, i got that.\\ni wanted s...         1707049256   \n",
       "6  amazing!!! at first i could not punch but i ev...         1707038018   \n",
       "7          it's just super fun. i love pvpin friends         1707030713   \n",
       "8  it is so fun to play with friends and even wit...         1707019575   \n",
       "9             good game, despite all the controversy         1707011714   \n",
       "\n",
       "   timestamp_updated  voted_up  votes_up  votes_funny  weighted_vote_score  \\\n",
       "0         1707077354      True         0            0                  0.0   \n",
       "1         1707076676      True         0            0                  0.0   \n",
       "2         1707072837      True         0            1                  0.0   \n",
       "3         1707068019      True         0            0                  0.0   \n",
       "4         1707064205     False         0            0                  0.0   \n",
       "5         1707049256      True         0            1                  0.0   \n",
       "6         1707038018      True         0            0                  0.0   \n",
       "7         1707030713      True         0            0                  0.0   \n",
       "8         1707019575      True         0            0                  0.0   \n",
       "9         1707011714      True         0            0                  0.0   \n",
       "\n",
       "   comment_count  ...            steamid  num_games_owned  num_reviews  \\\n",
       "0              0  ...  76561199401546423                0            1   \n",
       "1              0  ...  76561198056822321                0            2   \n",
       "2              0  ...  76561198428748427                0            1   \n",
       "3              0  ...  76561197987140501                0           10   \n",
       "4              0  ...  76561198024864749              375           17   \n",
       "5              0  ...  76561198186335569               85           31   \n",
       "6              0  ...  76561199554790429                0            1   \n",
       "7              0  ...  76561199503158814                0            1   \n",
       "8              0  ...  76561199616500100               11            1   \n",
       "9              0  ...  76561197984794794                0            4   \n",
       "\n",
       "   playtime_forever  playtime_last_two_weeks  playtime_at_review last_played  \\\n",
       "0              2027                      322                1986  1707079884   \n",
       "1               316                      316                 316  1707076353   \n",
       "2               847                      410                 804  1707075660   \n",
       "3              7864                      600                7712  1707109536   \n",
       "4              1456                        0                1456  1701029993   \n",
       "5                77                       77                  46  1707116433   \n",
       "6               302                      302                 302  1707007872   \n",
       "7              3482                     1295                3316  1707116694   \n",
       "8               358                      358                 318  1707081153   \n",
       "9               423                       21                 423  1707011693   \n",
       "\n",
       "                                 attributes_patterns  \\\n",
       "0              ['long day', 'fun easy', 'easy game']   \n",
       "1  ['party funny', 'funny mechanics', 'short whil...   \n",
       "2                                                 []   \n",
       "3               ['fun game', 'new skins/characters']   \n",
       "4         ['new content', 'old level', 'social hub']   \n",
       "5          ['silly game', 'non gamers', 'free pass']   \n",
       "6                        ['first i', 'same problem']   \n",
       "7                                      ['super fun']   \n",
       "8                                                 []   \n",
       "9                                      ['good game']   \n",
       "\n",
       "                                             POS_all  cluster  \n",
       "0  ['fun', 'game', 'play', \"'ve\", 'had', 'long', ...        1  \n",
       "1  ['pros', 'animals', 'party', 'funny', 'mechani...        1  \n",
       "2          ['drop', 'kick', 'cat', 'feeling', 'bad']        1  \n",
       "3  ['very', 'fun', 'game', 'animals', 'are', 'ver...        0  \n",
       "4  ['game', 'has', 'been', 'months', 'only', 'add...        1  \n",
       "5  ['i', 'wanted', 'silly', 'game', 'i', 'got', '...        1  \n",
       "6  ['amazing', 'first', 'i', 'not', 'punch', 'i',...        1  \n",
       "7  [\"'s\", 'just', 'super', 'fun', 'i', 'love', 'p...        0  \n",
       "8  ['is', 'so', 'fun', 'play', 'friends', 'even',...        1  \n",
       "9                    ['good', 'game', 'controversy']        1  \n",
       "\n",
       "[10 rows x 27 columns]"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "# Read the CSV file\n",
    "df = pd.read_csv('user_segmentation.csv')\n",
    "\n",
    "# Display the first 10 rows\n",
    "df.head(10)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Make sure lowercased all words except header"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/tr/psvn_y8x0w34ssccnb237zq00000gn/T/ipykernel_3719/2340421553.py:1: FutureWarning: DataFrame.applymap has been deprecated. Use DataFrame.map instead.\n",
      "  df = df.applymap(lambda x: x.lower() if isinstance(x, str) else x)\n"
     ]
    }
   ],
   "source": [
    "df = df.applymap(lambda x: x.lower() if isinstance(x, str) else x)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Sentiment Analysis on Nouns POS with Vader"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>recommendationid</th>\n",
       "      <th>language</th>\n",
       "      <th>review</th>\n",
       "      <th>timestamp_created</th>\n",
       "      <th>timestamp_updated</th>\n",
       "      <th>voted_up</th>\n",
       "      <th>votes_up</th>\n",
       "      <th>votes_funny</th>\n",
       "      <th>weighted_vote_score</th>\n",
       "      <th>comment_count</th>\n",
       "      <th>...</th>\n",
       "      <th>num_reviews</th>\n",
       "      <th>playtime_forever</th>\n",
       "      <th>playtime_last_two_weeks</th>\n",
       "      <th>playtime_at_review</th>\n",
       "      <th>last_played</th>\n",
       "      <th>attributes_patterns</th>\n",
       "      <th>POS_all</th>\n",
       "      <th>cluster</th>\n",
       "      <th>sentiment_scores_noun_pos_vader</th>\n",
       "      <th>sentiment_score_noun_pos_vader</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>157603005</td>\n",
       "      <td>english</td>\n",
       "      <td>fun game to play when you've had a long day an...</td>\n",
       "      <td>1707077354</td>\n",
       "      <td>1707077354</td>\n",
       "      <td>True</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>2027</td>\n",
       "      <td>322</td>\n",
       "      <td>1986</td>\n",
       "      <td>1707079884</td>\n",
       "      <td>['long day', 'fun easy', 'easy game']</td>\n",
       "      <td>['fun', 'game', 'play', \"'ve\", 'had', 'long', ...</td>\n",
       "      <td>1</td>\n",
       "      <td>{'neg': 0.0, 'neu': 0.392, 'pos': 0.608, 'comp...</td>\n",
       "      <td>0.7351</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>157602265</td>\n",
       "      <td>english</td>\n",
       "      <td>pros:\\nanimals\\nparty\\nfunny mechanics\\na bunc...</td>\n",
       "      <td>1707076676</td>\n",
       "      <td>1707076676</td>\n",
       "      <td>True</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>2</td>\n",
       "      <td>316</td>\n",
       "      <td>316</td>\n",
       "      <td>316</td>\n",
       "      <td>1707076353</td>\n",
       "      <td>['party funny', 'funny mechanics', 'short whil...</td>\n",
       "      <td>['pros', 'animals', 'party', 'funny', 'mechani...</td>\n",
       "      <td>1</td>\n",
       "      <td>{'neg': 0.0, 'neu': 0.854, 'pos': 0.146, 'comp...</td>\n",
       "      <td>0.4404</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>157598111</td>\n",
       "      <td>english</td>\n",
       "      <td>10/10 would drop kick a cat without feeling bad</td>\n",
       "      <td>1707072837</td>\n",
       "      <td>1707072837</td>\n",
       "      <td>True</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>847</td>\n",
       "      <td>410</td>\n",
       "      <td>804</td>\n",
       "      <td>1707075660</td>\n",
       "      <td>[]</td>\n",
       "      <td>['drop', 'kick', 'cat', 'feeling', 'bad']</td>\n",
       "      <td>1</td>\n",
       "      <td>{'neg': 0.0, 'neu': 1.0, 'pos': 0.0, 'compound...</td>\n",
       "      <td>0.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>157592673</td>\n",
       "      <td>english</td>\n",
       "      <td>very fun game. the animals are very cute, the ...</td>\n",
       "      <td>1707068019</td>\n",
       "      <td>1707068019</td>\n",
       "      <td>True</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>10</td>\n",
       "      <td>7864</td>\n",
       "      <td>600</td>\n",
       "      <td>7712</td>\n",
       "      <td>1707109536</td>\n",
       "      <td>['fun game', 'new skins/characters']</td>\n",
       "      <td>['very', 'fun', 'game', 'animals', 'are', 'ver...</td>\n",
       "      <td>0</td>\n",
       "      <td>{'neg': 0.0, 'neu': 1.0, 'pos': 0.0, 'compound...</td>\n",
       "      <td>0.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>157588222</td>\n",
       "      <td>english</td>\n",
       "      <td>game has been out for 4 months and they only a...</td>\n",
       "      <td>1707064205</td>\n",
       "      <td>1707064205</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>17</td>\n",
       "      <td>1456</td>\n",
       "      <td>0</td>\n",
       "      <td>1456</td>\n",
       "      <td>1701029993</td>\n",
       "      <td>['new content', 'old level', 'social hub']</td>\n",
       "      <td>['game', 'has', 'been', 'months', 'only', 'add...</td>\n",
       "      <td>1</td>\n",
       "      <td>{'neg': 0.0, 'neu': 1.0, 'pos': 0.0, 'compound...</td>\n",
       "      <td>0.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>157571234</td>\n",
       "      <td>english</td>\n",
       "      <td>i wanted a silly game, i got that.\\ni wanted s...</td>\n",
       "      <td>1707049256</td>\n",
       "      <td>1707049256</td>\n",
       "      <td>True</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>31</td>\n",
       "      <td>77</td>\n",
       "      <td>77</td>\n",
       "      <td>46</td>\n",
       "      <td>1707116433</td>\n",
       "      <td>['silly game', 'non gamers', 'free pass']</td>\n",
       "      <td>['i', 'wanted', 'silly', 'game', 'i', 'got', '...</td>\n",
       "      <td>1</td>\n",
       "      <td>{'neg': 0.0, 'neu': 0.602, 'pos': 0.398, 'comp...</td>\n",
       "      <td>0.5106</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>157560216</td>\n",
       "      <td>english</td>\n",
       "      <td>amazing!!! at first i could not punch but i ev...</td>\n",
       "      <td>1707038018</td>\n",
       "      <td>1707038018</td>\n",
       "      <td>True</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>302</td>\n",
       "      <td>302</td>\n",
       "      <td>302</td>\n",
       "      <td>1707007872</td>\n",
       "      <td>['first i', 'same problem']</td>\n",
       "      <td>['amazing', 'first', 'i', 'not', 'punch', 'i',...</td>\n",
       "      <td>1</td>\n",
       "      <td>{'neg': 0.0, 'neu': 1.0, 'pos': 0.0, 'compound...</td>\n",
       "      <td>0.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>157554594</td>\n",
       "      <td>english</td>\n",
       "      <td>it's just super fun. i love pvpin friends</td>\n",
       "      <td>1707030713</td>\n",
       "      <td>1707030713</td>\n",
       "      <td>True</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>3482</td>\n",
       "      <td>1295</td>\n",
       "      <td>3316</td>\n",
       "      <td>1707116694</td>\n",
       "      <td>['super fun']</td>\n",
       "      <td>[\"'s\", 'just', 'super', 'fun', 'i', 'love', 'p...</td>\n",
       "      <td>0</td>\n",
       "      <td>{'neg': 0.0, 'neu': 1.0, 'pos': 0.0, 'compound...</td>\n",
       "      <td>0.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>157545950</td>\n",
       "      <td>english</td>\n",
       "      <td>it is so fun to play with friends and even wit...</td>\n",
       "      <td>1707019575</td>\n",
       "      <td>1707019575</td>\n",
       "      <td>True</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>358</td>\n",
       "      <td>358</td>\n",
       "      <td>318</td>\n",
       "      <td>1707081153</td>\n",
       "      <td>[]</td>\n",
       "      <td>['is', 'so', 'fun', 'play', 'friends', 'even',...</td>\n",
       "      <td>1</td>\n",
       "      <td>{'neg': 0.0, 'neu': 1.0, 'pos': 0.0, 'compound...</td>\n",
       "      <td>0.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>157539476</td>\n",
       "      <td>english</td>\n",
       "      <td>good game, despite all the controversy</td>\n",
       "      <td>1707011714</td>\n",
       "      <td>1707011714</td>\n",
       "      <td>True</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>4</td>\n",
       "      <td>423</td>\n",
       "      <td>21</td>\n",
       "      <td>423</td>\n",
       "      <td>1707011693</td>\n",
       "      <td>['good game']</td>\n",
       "      <td>['good', 'game', 'controversy']</td>\n",
       "      <td>1</td>\n",
       "      <td>{'neg': 0.0, 'neu': 1.0, 'pos': 0.0, 'compound...</td>\n",
       "      <td>0.0000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>10 rows × 29 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   recommendationid language  \\\n",
       "0         157603005  english   \n",
       "1         157602265  english   \n",
       "2         157598111  english   \n",
       "3         157592673  english   \n",
       "4         157588222  english   \n",
       "5         157571234  english   \n",
       "6         157560216  english   \n",
       "7         157554594  english   \n",
       "8         157545950  english   \n",
       "9         157539476  english   \n",
       "\n",
       "                                              review  timestamp_created  \\\n",
       "0  fun game to play when you've had a long day an...         1707077354   \n",
       "1  pros:\\nanimals\\nparty\\nfunny mechanics\\na bunc...         1707076676   \n",
       "2    10/10 would drop kick a cat without feeling bad         1707072837   \n",
       "3  very fun game. the animals are very cute, the ...         1707068019   \n",
       "4  game has been out for 4 months and they only a...         1707064205   \n",
       "5  i wanted a silly game, i got that.\\ni wanted s...         1707049256   \n",
       "6  amazing!!! at first i could not punch but i ev...         1707038018   \n",
       "7          it's just super fun. i love pvpin friends         1707030713   \n",
       "8  it is so fun to play with friends and even wit...         1707019575   \n",
       "9             good game, despite all the controversy         1707011714   \n",
       "\n",
       "   timestamp_updated  voted_up  votes_up  votes_funny  weighted_vote_score  \\\n",
       "0         1707077354      True         0            0                  0.0   \n",
       "1         1707076676      True         0            0                  0.0   \n",
       "2         1707072837      True         0            1                  0.0   \n",
       "3         1707068019      True         0            0                  0.0   \n",
       "4         1707064205     False         0            0                  0.0   \n",
       "5         1707049256      True         0            1                  0.0   \n",
       "6         1707038018      True         0            0                  0.0   \n",
       "7         1707030713      True         0            0                  0.0   \n",
       "8         1707019575      True         0            0                  0.0   \n",
       "9         1707011714      True         0            0                  0.0   \n",
       "\n",
       "   comment_count  ...  num_reviews  playtime_forever  playtime_last_two_weeks  \\\n",
       "0              0  ...            1              2027                      322   \n",
       "1              0  ...            2               316                      316   \n",
       "2              0  ...            1               847                      410   \n",
       "3              0  ...           10              7864                      600   \n",
       "4              0  ...           17              1456                        0   \n",
       "5              0  ...           31                77                       77   \n",
       "6              0  ...            1               302                      302   \n",
       "7              0  ...            1              3482                     1295   \n",
       "8              0  ...            1               358                      358   \n",
       "9              0  ...            4               423                       21   \n",
       "\n",
       "   playtime_at_review  last_played  \\\n",
       "0                1986   1707079884   \n",
       "1                 316   1707076353   \n",
       "2                 804   1707075660   \n",
       "3                7712   1707109536   \n",
       "4                1456   1701029993   \n",
       "5                  46   1707116433   \n",
       "6                 302   1707007872   \n",
       "7                3316   1707116694   \n",
       "8                 318   1707081153   \n",
       "9                 423   1707011693   \n",
       "\n",
       "                                 attributes_patterns  \\\n",
       "0              ['long day', 'fun easy', 'easy game']   \n",
       "1  ['party funny', 'funny mechanics', 'short whil...   \n",
       "2                                                 []   \n",
       "3               ['fun game', 'new skins/characters']   \n",
       "4         ['new content', 'old level', 'social hub']   \n",
       "5          ['silly game', 'non gamers', 'free pass']   \n",
       "6                        ['first i', 'same problem']   \n",
       "7                                      ['super fun']   \n",
       "8                                                 []   \n",
       "9                                      ['good game']   \n",
       "\n",
       "                                             POS_all  cluster  \\\n",
       "0  ['fun', 'game', 'play', \"'ve\", 'had', 'long', ...        1   \n",
       "1  ['pros', 'animals', 'party', 'funny', 'mechani...        1   \n",
       "2          ['drop', 'kick', 'cat', 'feeling', 'bad']        1   \n",
       "3  ['very', 'fun', 'game', 'animals', 'are', 'ver...        0   \n",
       "4  ['game', 'has', 'been', 'months', 'only', 'add...        1   \n",
       "5  ['i', 'wanted', 'silly', 'game', 'i', 'got', '...        1   \n",
       "6  ['amazing', 'first', 'i', 'not', 'punch', 'i',...        1   \n",
       "7  [\"'s\", 'just', 'super', 'fun', 'i', 'love', 'p...        0   \n",
       "8  ['is', 'so', 'fun', 'play', 'friends', 'even',...        1   \n",
       "9                    ['good', 'game', 'controversy']        1   \n",
       "\n",
       "                     sentiment_scores_noun_pos_vader  \\\n",
       "0  {'neg': 0.0, 'neu': 0.392, 'pos': 0.608, 'comp...   \n",
       "1  {'neg': 0.0, 'neu': 0.854, 'pos': 0.146, 'comp...   \n",
       "2  {'neg': 0.0, 'neu': 1.0, 'pos': 0.0, 'compound...   \n",
       "3  {'neg': 0.0, 'neu': 1.0, 'pos': 0.0, 'compound...   \n",
       "4  {'neg': 0.0, 'neu': 1.0, 'pos': 0.0, 'compound...   \n",
       "5  {'neg': 0.0, 'neu': 0.602, 'pos': 0.398, 'comp...   \n",
       "6  {'neg': 0.0, 'neu': 1.0, 'pos': 0.0, 'compound...   \n",
       "7  {'neg': 0.0, 'neu': 1.0, 'pos': 0.0, 'compound...   \n",
       "8  {'neg': 0.0, 'neu': 1.0, 'pos': 0.0, 'compound...   \n",
       "9  {'neg': 0.0, 'neu': 1.0, 'pos': 0.0, 'compound...   \n",
       "\n",
       "   sentiment_score_noun_pos_vader  \n",
       "0                          0.7351  \n",
       "1                          0.4404  \n",
       "2                          0.0000  \n",
       "3                          0.0000  \n",
       "4                          0.0000  \n",
       "5                          0.5106  \n",
       "6                          0.0000  \n",
       "7                          0.0000  \n",
       "8                          0.0000  \n",
       "9                          0.0000  \n",
       "\n",
       "[10 rows x 29 columns]"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "from nltk.sentiment import SentimentIntensityAnalyzer\n",
    "\n",
    "# Load the VADER sentiment analyzer\n",
    "sia = SentimentIntensityAnalyzer()\n",
    "\n",
    "# Perform sentiment analysis Nouns POS \n",
    "df['sentiment_scores_noun_pos_vader'] = df['attributes_patterns'].apply(lambda x: sia.polarity_scores(x))\n",
    "df['sentiment_score_noun_pos_vader'] = df['sentiment_scores_noun_pos_vader'].apply(lambda x: x['compound'])\n",
    "\n",
    "df.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Sentiment Analysis on All POS with Vader"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>recommendationid</th>\n",
       "      <th>language</th>\n",
       "      <th>review</th>\n",
       "      <th>timestamp_created</th>\n",
       "      <th>timestamp_updated</th>\n",
       "      <th>voted_up</th>\n",
       "      <th>votes_up</th>\n",
       "      <th>votes_funny</th>\n",
       "      <th>weighted_vote_score</th>\n",
       "      <th>comment_count</th>\n",
       "      <th>...</th>\n",
       "      <th>playtime_last_two_weeks</th>\n",
       "      <th>playtime_at_review</th>\n",
       "      <th>last_played</th>\n",
       "      <th>attributes_patterns</th>\n",
       "      <th>POS_all</th>\n",
       "      <th>cluster</th>\n",
       "      <th>sentiment_scores_noun_pos_vader</th>\n",
       "      <th>sentiment_score_noun_pos_vader</th>\n",
       "      <th>sentiment_scores_all_pos_vader</th>\n",
       "      <th>sentiment_score_all_pos_vader</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>157603005</td>\n",
       "      <td>english</td>\n",
       "      <td>fun game to play when you've had a long day an...</td>\n",
       "      <td>1707077354</td>\n",
       "      <td>1707077354</td>\n",
       "      <td>True</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>322</td>\n",
       "      <td>1986</td>\n",
       "      <td>1707079884</td>\n",
       "      <td>['long day', 'fun easy', 'easy game']</td>\n",
       "      <td>['fun', 'game', 'play', \"'ve\", 'had', 'long', ...</td>\n",
       "      <td>1</td>\n",
       "      <td>{'neg': 0.0, 'neu': 0.392, 'pos': 0.608, 'comp...</td>\n",
       "      <td>0.7351</td>\n",
       "      <td>{'neg': 0.0, 'neu': 1.0, 'pos': 0.0, 'compound...</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>157602265</td>\n",
       "      <td>english</td>\n",
       "      <td>pros:\\nanimals\\nparty\\nfunny mechanics\\na bunc...</td>\n",
       "      <td>1707076676</td>\n",
       "      <td>1707076676</td>\n",
       "      <td>True</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>316</td>\n",
       "      <td>316</td>\n",
       "      <td>1707076353</td>\n",
       "      <td>['party funny', 'funny mechanics', 'short whil...</td>\n",
       "      <td>['pros', 'animals', 'party', 'funny', 'mechani...</td>\n",
       "      <td>1</td>\n",
       "      <td>{'neg': 0.0, 'neu': 0.854, 'pos': 0.146, 'comp...</td>\n",
       "      <td>0.4404</td>\n",
       "      <td>{'neg': 0.0, 'neu': 1.0, 'pos': 0.0, 'compound...</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>157598111</td>\n",
       "      <td>english</td>\n",
       "      <td>10/10 would drop kick a cat without feeling bad</td>\n",
       "      <td>1707072837</td>\n",
       "      <td>1707072837</td>\n",
       "      <td>True</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>410</td>\n",
       "      <td>804</td>\n",
       "      <td>1707075660</td>\n",
       "      <td>[]</td>\n",
       "      <td>['drop', 'kick', 'cat', 'feeling', 'bad']</td>\n",
       "      <td>1</td>\n",
       "      <td>{'neg': 0.0, 'neu': 1.0, 'pos': 0.0, 'compound...</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>{'neg': 0.0, 'neu': 1.0, 'pos': 0.0, 'compound...</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>157592673</td>\n",
       "      <td>english</td>\n",
       "      <td>very fun game. the animals are very cute, the ...</td>\n",
       "      <td>1707068019</td>\n",
       "      <td>1707068019</td>\n",
       "      <td>True</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>600</td>\n",
       "      <td>7712</td>\n",
       "      <td>1707109536</td>\n",
       "      <td>['fun game', 'new skins/characters']</td>\n",
       "      <td>['very', 'fun', 'game', 'animals', 'are', 'ver...</td>\n",
       "      <td>0</td>\n",
       "      <td>{'neg': 0.0, 'neu': 1.0, 'pos': 0.0, 'compound...</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>{'neg': 0.0, 'neu': 1.0, 'pos': 0.0, 'compound...</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>157588222</td>\n",
       "      <td>english</td>\n",
       "      <td>game has been out for 4 months and they only a...</td>\n",
       "      <td>1707064205</td>\n",
       "      <td>1707064205</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1456</td>\n",
       "      <td>1701029993</td>\n",
       "      <td>['new content', 'old level', 'social hub']</td>\n",
       "      <td>['game', 'has', 'been', 'months', 'only', 'add...</td>\n",
       "      <td>1</td>\n",
       "      <td>{'neg': 0.0, 'neu': 1.0, 'pos': 0.0, 'compound...</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>{'neg': 0.0, 'neu': 1.0, 'pos': 0.0, 'compound...</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>157571234</td>\n",
       "      <td>english</td>\n",
       "      <td>i wanted a silly game, i got that.\\ni wanted s...</td>\n",
       "      <td>1707049256</td>\n",
       "      <td>1707049256</td>\n",
       "      <td>True</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>77</td>\n",
       "      <td>46</td>\n",
       "      <td>1707116433</td>\n",
       "      <td>['silly game', 'non gamers', 'free pass']</td>\n",
       "      <td>['i', 'wanted', 'silly', 'game', 'i', 'got', '...</td>\n",
       "      <td>1</td>\n",
       "      <td>{'neg': 0.0, 'neu': 0.602, 'pos': 0.398, 'comp...</td>\n",
       "      <td>0.5106</td>\n",
       "      <td>{'neg': 0.0, 'neu': 1.0, 'pos': 0.0, 'compound...</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>157560216</td>\n",
       "      <td>english</td>\n",
       "      <td>amazing!!! at first i could not punch but i ev...</td>\n",
       "      <td>1707038018</td>\n",
       "      <td>1707038018</td>\n",
       "      <td>True</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>302</td>\n",
       "      <td>302</td>\n",
       "      <td>1707007872</td>\n",
       "      <td>['first i', 'same problem']</td>\n",
       "      <td>['amazing', 'first', 'i', 'not', 'punch', 'i',...</td>\n",
       "      <td>1</td>\n",
       "      <td>{'neg': 0.0, 'neu': 1.0, 'pos': 0.0, 'compound...</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>{'neg': 0.0, 'neu': 1.0, 'pos': 0.0, 'compound...</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>157554594</td>\n",
       "      <td>english</td>\n",
       "      <td>it's just super fun. i love pvpin friends</td>\n",
       "      <td>1707030713</td>\n",
       "      <td>1707030713</td>\n",
       "      <td>True</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>1295</td>\n",
       "      <td>3316</td>\n",
       "      <td>1707116694</td>\n",
       "      <td>['super fun']</td>\n",
       "      <td>[\"'s\", 'just', 'super', 'fun', 'i', 'love', 'p...</td>\n",
       "      <td>0</td>\n",
       "      <td>{'neg': 0.0, 'neu': 1.0, 'pos': 0.0, 'compound...</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>{'neg': 0.0, 'neu': 1.0, 'pos': 0.0, 'compound...</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>157545950</td>\n",
       "      <td>english</td>\n",
       "      <td>it is so fun to play with friends and even wit...</td>\n",
       "      <td>1707019575</td>\n",
       "      <td>1707019575</td>\n",
       "      <td>True</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>358</td>\n",
       "      <td>318</td>\n",
       "      <td>1707081153</td>\n",
       "      <td>[]</td>\n",
       "      <td>['is', 'so', 'fun', 'play', 'friends', 'even',...</td>\n",
       "      <td>1</td>\n",
       "      <td>{'neg': 0.0, 'neu': 1.0, 'pos': 0.0, 'compound...</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>{'neg': 0.0, 'neu': 1.0, 'pos': 0.0, 'compound...</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>157539476</td>\n",
       "      <td>english</td>\n",
       "      <td>good game, despite all the controversy</td>\n",
       "      <td>1707011714</td>\n",
       "      <td>1707011714</td>\n",
       "      <td>True</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>21</td>\n",
       "      <td>423</td>\n",
       "      <td>1707011693</td>\n",
       "      <td>['good game']</td>\n",
       "      <td>['good', 'game', 'controversy']</td>\n",
       "      <td>1</td>\n",
       "      <td>{'neg': 0.0, 'neu': 1.0, 'pos': 0.0, 'compound...</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>{'neg': 0.0, 'neu': 1.0, 'pos': 0.0, 'compound...</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>10 rows × 31 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   recommendationid language  \\\n",
       "0         157603005  english   \n",
       "1         157602265  english   \n",
       "2         157598111  english   \n",
       "3         157592673  english   \n",
       "4         157588222  english   \n",
       "5         157571234  english   \n",
       "6         157560216  english   \n",
       "7         157554594  english   \n",
       "8         157545950  english   \n",
       "9         157539476  english   \n",
       "\n",
       "                                              review  timestamp_created  \\\n",
       "0  fun game to play when you've had a long day an...         1707077354   \n",
       "1  pros:\\nanimals\\nparty\\nfunny mechanics\\na bunc...         1707076676   \n",
       "2    10/10 would drop kick a cat without feeling bad         1707072837   \n",
       "3  very fun game. the animals are very cute, the ...         1707068019   \n",
       "4  game has been out for 4 months and they only a...         1707064205   \n",
       "5  i wanted a silly game, i got that.\\ni wanted s...         1707049256   \n",
       "6  amazing!!! at first i could not punch but i ev...         1707038018   \n",
       "7          it's just super fun. i love pvpin friends         1707030713   \n",
       "8  it is so fun to play with friends and even wit...         1707019575   \n",
       "9             good game, despite all the controversy         1707011714   \n",
       "\n",
       "   timestamp_updated  voted_up  votes_up  votes_funny  weighted_vote_score  \\\n",
       "0         1707077354      True         0            0                  0.0   \n",
       "1         1707076676      True         0            0                  0.0   \n",
       "2         1707072837      True         0            1                  0.0   \n",
       "3         1707068019      True         0            0                  0.0   \n",
       "4         1707064205     False         0            0                  0.0   \n",
       "5         1707049256      True         0            1                  0.0   \n",
       "6         1707038018      True         0            0                  0.0   \n",
       "7         1707030713      True         0            0                  0.0   \n",
       "8         1707019575      True         0            0                  0.0   \n",
       "9         1707011714      True         0            0                  0.0   \n",
       "\n",
       "   comment_count  ...  playtime_last_two_weeks  playtime_at_review  \\\n",
       "0              0  ...                      322                1986   \n",
       "1              0  ...                      316                 316   \n",
       "2              0  ...                      410                 804   \n",
       "3              0  ...                      600                7712   \n",
       "4              0  ...                        0                1456   \n",
       "5              0  ...                       77                  46   \n",
       "6              0  ...                      302                 302   \n",
       "7              0  ...                     1295                3316   \n",
       "8              0  ...                      358                 318   \n",
       "9              0  ...                       21                 423   \n",
       "\n",
       "   last_played                                attributes_patterns  \\\n",
       "0   1707079884              ['long day', 'fun easy', 'easy game']   \n",
       "1   1707076353  ['party funny', 'funny mechanics', 'short whil...   \n",
       "2   1707075660                                                 []   \n",
       "3   1707109536               ['fun game', 'new skins/characters']   \n",
       "4   1701029993         ['new content', 'old level', 'social hub']   \n",
       "5   1707116433          ['silly game', 'non gamers', 'free pass']   \n",
       "6   1707007872                        ['first i', 'same problem']   \n",
       "7   1707116694                                      ['super fun']   \n",
       "8   1707081153                                                 []   \n",
       "9   1707011693                                      ['good game']   \n",
       "\n",
       "                                             POS_all  cluster  \\\n",
       "0  ['fun', 'game', 'play', \"'ve\", 'had', 'long', ...        1   \n",
       "1  ['pros', 'animals', 'party', 'funny', 'mechani...        1   \n",
       "2          ['drop', 'kick', 'cat', 'feeling', 'bad']        1   \n",
       "3  ['very', 'fun', 'game', 'animals', 'are', 'ver...        0   \n",
       "4  ['game', 'has', 'been', 'months', 'only', 'add...        1   \n",
       "5  ['i', 'wanted', 'silly', 'game', 'i', 'got', '...        1   \n",
       "6  ['amazing', 'first', 'i', 'not', 'punch', 'i',...        1   \n",
       "7  [\"'s\", 'just', 'super', 'fun', 'i', 'love', 'p...        0   \n",
       "8  ['is', 'so', 'fun', 'play', 'friends', 'even',...        1   \n",
       "9                    ['good', 'game', 'controversy']        1   \n",
       "\n",
       "                     sentiment_scores_noun_pos_vader  \\\n",
       "0  {'neg': 0.0, 'neu': 0.392, 'pos': 0.608, 'comp...   \n",
       "1  {'neg': 0.0, 'neu': 0.854, 'pos': 0.146, 'comp...   \n",
       "2  {'neg': 0.0, 'neu': 1.0, 'pos': 0.0, 'compound...   \n",
       "3  {'neg': 0.0, 'neu': 1.0, 'pos': 0.0, 'compound...   \n",
       "4  {'neg': 0.0, 'neu': 1.0, 'pos': 0.0, 'compound...   \n",
       "5  {'neg': 0.0, 'neu': 0.602, 'pos': 0.398, 'comp...   \n",
       "6  {'neg': 0.0, 'neu': 1.0, 'pos': 0.0, 'compound...   \n",
       "7  {'neg': 0.0, 'neu': 1.0, 'pos': 0.0, 'compound...   \n",
       "8  {'neg': 0.0, 'neu': 1.0, 'pos': 0.0, 'compound...   \n",
       "9  {'neg': 0.0, 'neu': 1.0, 'pos': 0.0, 'compound...   \n",
       "\n",
       "   sentiment_score_noun_pos_vader  \\\n",
       "0                          0.7351   \n",
       "1                          0.4404   \n",
       "2                          0.0000   \n",
       "3                          0.0000   \n",
       "4                          0.0000   \n",
       "5                          0.5106   \n",
       "6                          0.0000   \n",
       "7                          0.0000   \n",
       "8                          0.0000   \n",
       "9                          0.0000   \n",
       "\n",
       "                      sentiment_scores_all_pos_vader  \\\n",
       "0  {'neg': 0.0, 'neu': 1.0, 'pos': 0.0, 'compound...   \n",
       "1  {'neg': 0.0, 'neu': 1.0, 'pos': 0.0, 'compound...   \n",
       "2  {'neg': 0.0, 'neu': 1.0, 'pos': 0.0, 'compound...   \n",
       "3  {'neg': 0.0, 'neu': 1.0, 'pos': 0.0, 'compound...   \n",
       "4  {'neg': 0.0, 'neu': 1.0, 'pos': 0.0, 'compound...   \n",
       "5  {'neg': 0.0, 'neu': 1.0, 'pos': 0.0, 'compound...   \n",
       "6  {'neg': 0.0, 'neu': 1.0, 'pos': 0.0, 'compound...   \n",
       "7  {'neg': 0.0, 'neu': 1.0, 'pos': 0.0, 'compound...   \n",
       "8  {'neg': 0.0, 'neu': 1.0, 'pos': 0.0, 'compound...   \n",
       "9  {'neg': 0.0, 'neu': 1.0, 'pos': 0.0, 'compound...   \n",
       "\n",
       "   sentiment_score_all_pos_vader  \n",
       "0                            0.0  \n",
       "1                            0.0  \n",
       "2                            0.0  \n",
       "3                            0.0  \n",
       "4                            0.0  \n",
       "5                            0.0  \n",
       "6                            0.0  \n",
       "7                            0.0  \n",
       "8                            0.0  \n",
       "9                            0.0  \n",
       "\n",
       "[10 rows x 31 columns]"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Perform sentiment analysis All POS, including Nouns, Verbs, Adjectives, Adverbs, etc. \n",
    "df['sentiment_scores_all_pos_vader'] = df['POS_all'].apply(lambda x: sia.polarity_scores(x))\n",
    "df['sentiment_score_all_pos_vader'] = df['sentiment_scores_all_pos_vader'].apply(lambda x: x['compound'])\n",
    "\n",
    "df.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Sentiment Analysis on Entire Review with SpaCy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>recommendationid</th>\n",
       "      <th>language</th>\n",
       "      <th>review</th>\n",
       "      <th>timestamp_created</th>\n",
       "      <th>timestamp_updated</th>\n",
       "      <th>voted_up</th>\n",
       "      <th>votes_up</th>\n",
       "      <th>votes_funny</th>\n",
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       "      <th>comment_count</th>\n",
       "      <th>...</th>\n",
       "      <th>playtime_at_review</th>\n",
       "      <th>last_played</th>\n",
       "      <th>attributes_patterns</th>\n",
       "      <th>POS_all</th>\n",
       "      <th>cluster</th>\n",
       "      <th>sentiment_scores_noun_pos_vader</th>\n",
       "      <th>sentiment_score_noun_pos_vader</th>\n",
       "      <th>sentiment_scores_all_pos_vader</th>\n",
       "      <th>sentiment_score_all_pos_vader</th>\n",
       "      <th>sentiment_score_review_spacy</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>157603005</td>\n",
       "      <td>english</td>\n",
       "      <td>fun game to play when you've had a long day an...</td>\n",
       "      <td>1707077354</td>\n",
       "      <td>1707077354</td>\n",
       "      <td>True</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>1986</td>\n",
       "      <td>1707079884</td>\n",
       "      <td>['long day', 'fun easy', 'easy game']</td>\n",
       "      <td>['fun', 'game', 'play', \"'ve\", 'had', 'long', ...</td>\n",
       "      <td>1</td>\n",
       "      <td>{'neg': 0.0, 'neu': 0.392, 'pos': 0.608, 'comp...</td>\n",
       "      <td>0.7351</td>\n",
       "      <td>{'neg': 0.0, 'neu': 1.0, 'pos': 0.0, 'compound...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.031481</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>157602265</td>\n",
       "      <td>english</td>\n",
       "      <td>pros:\\nanimals\\nparty\\nfunny mechanics\\na bunc...</td>\n",
       "      <td>1707076676</td>\n",
       "      <td>1707076676</td>\n",
       "      <td>True</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>316</td>\n",
       "      <td>1707076353</td>\n",
       "      <td>['party funny', 'funny mechanics', 'short whil...</td>\n",
       "      <td>['pros', 'animals', 'party', 'funny', 'mechani...</td>\n",
       "      <td>1</td>\n",
       "      <td>{'neg': 0.0, 'neu': 0.854, 'pos': 0.146, 'comp...</td>\n",
       "      <td>0.4404</td>\n",
       "      <td>{'neg': 0.0, 'neu': 1.0, 'pos': 0.0, 'compound...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.000231</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>157598111</td>\n",
       "      <td>english</td>\n",
       "      <td>10/10 would drop kick a cat without feeling bad</td>\n",
       "      <td>1707072837</td>\n",
       "      <td>1707072837</td>\n",
       "      <td>True</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>804</td>\n",
       "      <td>1707075660</td>\n",
       "      <td>[]</td>\n",
       "      <td>['drop', 'kick', 'cat', 'feeling', 'bad']</td>\n",
       "      <td>1</td>\n",
       "      <td>{'neg': 0.0, 'neu': 1.0, 'pos': 0.0, 'compound...</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>{'neg': 0.0, 'neu': 1.0, 'pos': 0.0, 'compound...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.700000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>157592673</td>\n",
       "      <td>english</td>\n",
       "      <td>very fun game. the animals are very cute, the ...</td>\n",
       "      <td>1707068019</td>\n",
       "      <td>1707068019</td>\n",
       "      <td>True</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>7712</td>\n",
       "      <td>1707109536</td>\n",
       "      <td>['fun game', 'new skins/characters']</td>\n",
       "      <td>['very', 'fun', 'game', 'animals', 'are', 'ver...</td>\n",
       "      <td>0</td>\n",
       "      <td>{'neg': 0.0, 'neu': 1.0, 'pos': 0.0, 'compound...</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>{'neg': 0.0, 'neu': 1.0, 'pos': 0.0, 'compound...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.218052</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>157588222</td>\n",
       "      <td>english</td>\n",
       "      <td>game has been out for 4 months and they only a...</td>\n",
       "      <td>1707064205</td>\n",
       "      <td>1707064205</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>1456</td>\n",
       "      <td>1701029993</td>\n",
       "      <td>['new content', 'old level', 'social hub']</td>\n",
       "      <td>['game', 'has', 'been', 'months', 'only', 'add...</td>\n",
       "      <td>1</td>\n",
       "      <td>{'neg': 0.0, 'neu': 1.0, 'pos': 0.0, 'compound...</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>{'neg': 0.0, 'neu': 1.0, 'pos': 0.0, 'compound...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.116856</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 32 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   recommendationid language  \\\n",
       "0         157603005  english   \n",
       "1         157602265  english   \n",
       "2         157598111  english   \n",
       "3         157592673  english   \n",
       "4         157588222  english   \n",
       "\n",
       "                                              review  timestamp_created  \\\n",
       "0  fun game to play when you've had a long day an...         1707077354   \n",
       "1  pros:\\nanimals\\nparty\\nfunny mechanics\\na bunc...         1707076676   \n",
       "2    10/10 would drop kick a cat without feeling bad         1707072837   \n",
       "3  very fun game. the animals are very cute, the ...         1707068019   \n",
       "4  game has been out for 4 months and they only a...         1707064205   \n",
       "\n",
       "   timestamp_updated  voted_up  votes_up  votes_funny  weighted_vote_score  \\\n",
       "0         1707077354      True         0            0                  0.0   \n",
       "1         1707076676      True         0            0                  0.0   \n",
       "2         1707072837      True         0            1                  0.0   \n",
       "3         1707068019      True         0            0                  0.0   \n",
       "4         1707064205     False         0            0                  0.0   \n",
       "\n",
       "   comment_count  ...  playtime_at_review  last_played  \\\n",
       "0              0  ...                1986   1707079884   \n",
       "1              0  ...                 316   1707076353   \n",
       "2              0  ...                 804   1707075660   \n",
       "3              0  ...                7712   1707109536   \n",
       "4              0  ...                1456   1701029993   \n",
       "\n",
       "                                 attributes_patterns  \\\n",
       "0              ['long day', 'fun easy', 'easy game']   \n",
       "1  ['party funny', 'funny mechanics', 'short whil...   \n",
       "2                                                 []   \n",
       "3               ['fun game', 'new skins/characters']   \n",
       "4         ['new content', 'old level', 'social hub']   \n",
       "\n",
       "                                             POS_all  cluster  \\\n",
       "0  ['fun', 'game', 'play', \"'ve\", 'had', 'long', ...        1   \n",
       "1  ['pros', 'animals', 'party', 'funny', 'mechani...        1   \n",
       "2          ['drop', 'kick', 'cat', 'feeling', 'bad']        1   \n",
       "3  ['very', 'fun', 'game', 'animals', 'are', 'ver...        0   \n",
       "4  ['game', 'has', 'been', 'months', 'only', 'add...        1   \n",
       "\n",
       "                     sentiment_scores_noun_pos_vader  \\\n",
       "0  {'neg': 0.0, 'neu': 0.392, 'pos': 0.608, 'comp...   \n",
       "1  {'neg': 0.0, 'neu': 0.854, 'pos': 0.146, 'comp...   \n",
       "2  {'neg': 0.0, 'neu': 1.0, 'pos': 0.0, 'compound...   \n",
       "3  {'neg': 0.0, 'neu': 1.0, 'pos': 0.0, 'compound...   \n",
       "4  {'neg': 0.0, 'neu': 1.0, 'pos': 0.0, 'compound...   \n",
       "\n",
       "  sentiment_score_noun_pos_vader  \\\n",
       "0                         0.7351   \n",
       "1                         0.4404   \n",
       "2                         0.0000   \n",
       "3                         0.0000   \n",
       "4                         0.0000   \n",
       "\n",
       "                      sentiment_scores_all_pos_vader  \\\n",
       "0  {'neg': 0.0, 'neu': 1.0, 'pos': 0.0, 'compound...   \n",
       "1  {'neg': 0.0, 'neu': 1.0, 'pos': 0.0, 'compound...   \n",
       "2  {'neg': 0.0, 'neu': 1.0, 'pos': 0.0, 'compound...   \n",
       "3  {'neg': 0.0, 'neu': 1.0, 'pos': 0.0, 'compound...   \n",
       "4  {'neg': 0.0, 'neu': 1.0, 'pos': 0.0, 'compound...   \n",
       "\n",
       "   sentiment_score_all_pos_vader  sentiment_score_review_spacy  \n",
       "0                            0.0                      0.031481  \n",
       "1                            0.0                     -0.000231  \n",
       "2                            0.0                     -0.700000  \n",
       "3                            0.0                      0.218052  \n",
       "4                            0.0                     -0.116856  \n",
       "\n",
       "[5 rows x 32 columns]"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import spacy\n",
    "from spacytextblob.spacytextblob import SpacyTextBlob\n",
    "\n",
    "# Load the SpaCy model\n",
    "nlp = spacy.load('en_core_web_sm')\n",
    "\n",
    "# Add the SpaCyTextBlob component to the pipeline\n",
    "nlp.add_pipe('spacytextblob')\n",
    "\n",
    "# Perform sentiment analysis on the \"review\" column\n",
    "df['sentiment_score_review_spacy'] = df['review'].apply(lambda x: nlp(str(x))._.polarity)\n",
    "\n",
    "df.head()\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Sentiment Analysis on Nouns POS with SpaCy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>recommendationid</th>\n",
       "      <th>language</th>\n",
       "      <th>review</th>\n",
       "      <th>timestamp_created</th>\n",
       "      <th>timestamp_updated</th>\n",
       "      <th>voted_up</th>\n",
       "      <th>votes_up</th>\n",
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       "      <th>comment_count</th>\n",
       "      <th>...</th>\n",
       "      <th>last_played</th>\n",
       "      <th>attributes_patterns</th>\n",
       "      <th>POS_all</th>\n",
       "      <th>cluster</th>\n",
       "      <th>sentiment_scores_noun_pos_vader</th>\n",
       "      <th>sentiment_score_noun_pos_vader</th>\n",
       "      <th>sentiment_scores_all_pos_vader</th>\n",
       "      <th>sentiment_score_all_pos_vader</th>\n",
       "      <th>sentiment_score_review_spacy</th>\n",
       "      <th>sentiment_score_noun_pos_spacy</th>\n",
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       "      <td>157603005</td>\n",
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       "    <tr>\n",
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       "      <td>157602265</td>\n",
       "      <td>english</td>\n",
       "      <td>pros:\\nanimals\\nparty\\nfunny mechanics\\na bunc...</td>\n",
       "      <td>1707076676</td>\n",
       "      <td>1707076676</td>\n",
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       "      <td>['party funny', 'funny mechanics', 'short whil...</td>\n",
       "      <td>['pros', 'animals', 'party', 'funny', 'mechani...</td>\n",
       "      <td>1</td>\n",
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       "      <td>0.4404</td>\n",
       "      <td>{'neg': 0.0, 'neu': 1.0, 'pos': 0.0, 'compound...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.000231</td>\n",
       "      <td>0.166667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>157598111</td>\n",
       "      <td>english</td>\n",
       "      <td>10/10 would drop kick a cat without feeling bad</td>\n",
       "      <td>1707072837</td>\n",
       "      <td>1707072837</td>\n",
       "      <td>True</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>1707075660</td>\n",
       "      <td>[]</td>\n",
       "      <td>['drop', 'kick', 'cat', 'feeling', 'bad']</td>\n",
       "      <td>1</td>\n",
       "      <td>{'neg': 0.0, 'neu': 1.0, 'pos': 0.0, 'compound...</td>\n",
       "      <td>0.0000</td>\n",
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       "      <td>-0.700000</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>157592673</td>\n",
       "      <td>english</td>\n",
       "      <td>very fun game. the animals are very cute, the ...</td>\n",
       "      <td>1707068019</td>\n",
       "      <td>1707068019</td>\n",
       "      <td>True</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>1707109536</td>\n",
       "      <td>['fun game', 'new skins/characters']</td>\n",
       "      <td>['very', 'fun', 'game', 'animals', 'are', 'ver...</td>\n",
       "      <td>0</td>\n",
       "      <td>{'neg': 0.0, 'neu': 1.0, 'pos': 0.0, 'compound...</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>157588222</td>\n",
       "      <td>english</td>\n",
       "      <td>game has been out for 4 months and they only a...</td>\n",
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       "      <td>['new content', 'old level', 'social hub']</td>\n",
       "      <td>['game', 'has', 'been', 'months', 'only', 'add...</td>\n",
       "      <td>1</td>\n",
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       "      <td>0.0000</td>\n",
       "      <td>{'neg': 0.0, 'neu': 1.0, 'pos': 0.0, 'compound...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.116856</td>\n",
       "      <td>0.089899</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 33 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   recommendationid language  \\\n",
       "0         157603005  english   \n",
       "1         157602265  english   \n",
       "2         157598111  english   \n",
       "3         157592673  english   \n",
       "4         157588222  english   \n",
       "\n",
       "                                              review  timestamp_created  \\\n",
       "0  fun game to play when you've had a long day an...         1707077354   \n",
       "1  pros:\\nanimals\\nparty\\nfunny mechanics\\na bunc...         1707076676   \n",
       "2    10/10 would drop kick a cat without feeling bad         1707072837   \n",
       "3  very fun game. the animals are very cute, the ...         1707068019   \n",
       "4  game has been out for 4 months and they only a...         1707064205   \n",
       "\n",
       "   timestamp_updated  voted_up  votes_up  votes_funny  weighted_vote_score  \\\n",
       "0         1707077354      True         0            0                  0.0   \n",
       "1         1707076676      True         0            0                  0.0   \n",
       "2         1707072837      True         0            1                  0.0   \n",
       "3         1707068019      True         0            0                  0.0   \n",
       "4         1707064205     False         0            0                  0.0   \n",
       "\n",
       "   comment_count  ...  last_played  \\\n",
       "0              0  ...   1707079884   \n",
       "1              0  ...   1707076353   \n",
       "2              0  ...   1707075660   \n",
       "3              0  ...   1707109536   \n",
       "4              0  ...   1701029993   \n",
       "\n",
       "                                 attributes_patterns  \\\n",
       "0              ['long day', 'fun easy', 'easy game']   \n",
       "1  ['party funny', 'funny mechanics', 'short whil...   \n",
       "2                                                 []   \n",
       "3               ['fun game', 'new skins/characters']   \n",
       "4         ['new content', 'old level', 'social hub']   \n",
       "\n",
       "                                             POS_all  cluster  \\\n",
       "0  ['fun', 'game', 'play', \"'ve\", 'had', 'long', ...        1   \n",
       "1  ['pros', 'animals', 'party', 'funny', 'mechani...        1   \n",
       "2          ['drop', 'kick', 'cat', 'feeling', 'bad']        1   \n",
       "3  ['very', 'fun', 'game', 'animals', 'are', 'ver...        0   \n",
       "4  ['game', 'has', 'been', 'months', 'only', 'add...        1   \n",
       "\n",
       "                     sentiment_scores_noun_pos_vader  \\\n",
       "0  {'neg': 0.0, 'neu': 0.392, 'pos': 0.608, 'comp...   \n",
       "1  {'neg': 0.0, 'neu': 0.854, 'pos': 0.146, 'comp...   \n",
       "2  {'neg': 0.0, 'neu': 1.0, 'pos': 0.0, 'compound...   \n",
       "3  {'neg': 0.0, 'neu': 1.0, 'pos': 0.0, 'compound...   \n",
       "4  {'neg': 0.0, 'neu': 1.0, 'pos': 0.0, 'compound...   \n",
       "\n",
       "   sentiment_score_noun_pos_vader  \\\n",
       "0                          0.7351   \n",
       "1                          0.4404   \n",
       "2                          0.0000   \n",
       "3                          0.0000   \n",
       "4                          0.0000   \n",
       "\n",
       "                      sentiment_scores_all_pos_vader  \\\n",
       "0  {'neg': 0.0, 'neu': 1.0, 'pos': 0.0, 'compound...   \n",
       "1  {'neg': 0.0, 'neu': 1.0, 'pos': 0.0, 'compound...   \n",
       "2  {'neg': 0.0, 'neu': 1.0, 'pos': 0.0, 'compound...   \n",
       "3  {'neg': 0.0, 'neu': 1.0, 'pos': 0.0, 'compound...   \n",
       "4  {'neg': 0.0, 'neu': 1.0, 'pos': 0.0, 'compound...   \n",
       "\n",
       "   sentiment_score_all_pos_vader  sentiment_score_review_spacy  \\\n",
       "0                            0.0                      0.031481   \n",
       "1                            0.0                     -0.000231   \n",
       "2                            0.0                     -0.700000   \n",
       "3                            0.0                      0.218052   \n",
       "4                            0.0                     -0.116856   \n",
       "\n",
       "   sentiment_score_noun_pos_spacy  \n",
       "0                        0.143333  \n",
       "1                        0.166667  \n",
       "2                        0.000000  \n",
       "3                        0.012121  \n",
       "4                        0.089899  \n",
       "\n",
       "[5 rows x 33 columns]"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Perform sentiment analysis on the \"attributes_patterns\" column\n",
    "df['sentiment_score_noun_pos_spacy'] = df['attributes_patterns'].apply(lambda x: nlp(str(x))._.polarity)\n",
    "\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Sentiment Analysis on All POS with SpaCy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>recommendationid</th>\n",
       "      <th>language</th>\n",
       "      <th>review</th>\n",
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       "      <td>1707077354</td>\n",
       "      <td>1707077354</td>\n",
       "      <td>True</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>['long day', 'fun easy', 'easy game']</td>\n",
       "      <td>['fun', 'game', 'play', \"'ve\", 'had', 'long', ...</td>\n",
       "      <td>1</td>\n",
       "      <td>{'neg': 0.0, 'neu': 0.392, 'pos': 0.608, 'comp...</td>\n",
       "      <td>0.7351</td>\n",
       "      <td>{'neg': 0.0, 'neu': 1.0, 'pos': 0.0, 'compound...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.031481</td>\n",
       "      <td>0.143333</td>\n",
       "      <td>0.031481</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>157602265</td>\n",
       "      <td>english</td>\n",
       "      <td>pros:\\nanimals\\nparty\\nfunny mechanics\\na bunc...</td>\n",
       "      <td>1707076676</td>\n",
       "      <td>1707076676</td>\n",
       "      <td>True</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>['party funny', 'funny mechanics', 'short whil...</td>\n",
       "      <td>['pros', 'animals', 'party', 'funny', 'mechani...</td>\n",
       "      <td>1</td>\n",
       "      <td>{'neg': 0.0, 'neu': 0.854, 'pos': 0.146, 'comp...</td>\n",
       "      <td>0.4404</td>\n",
       "      <td>{'neg': 0.0, 'neu': 1.0, 'pos': 0.0, 'compound...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.000231</td>\n",
       "      <td>0.166667</td>\n",
       "      <td>-0.011719</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>157598111</td>\n",
       "      <td>english</td>\n",
       "      <td>10/10 would drop kick a cat without feeling bad</td>\n",
       "      <td>1707072837</td>\n",
       "      <td>1707072837</td>\n",
       "      <td>True</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>[]</td>\n",
       "      <td>['drop', 'kick', 'cat', 'feeling', 'bad']</td>\n",
       "      <td>1</td>\n",
       "      <td>{'neg': 0.0, 'neu': 1.0, 'pos': 0.0, 'compound...</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>{'neg': 0.0, 'neu': 1.0, 'pos': 0.0, 'compound...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.700000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-0.700000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>157592673</td>\n",
       "      <td>english</td>\n",
       "      <td>very fun game. the animals are very cute, the ...</td>\n",
       "      <td>1707068019</td>\n",
       "      <td>1707068019</td>\n",
       "      <td>True</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>['fun game', 'new skins/characters']</td>\n",
       "      <td>['very', 'fun', 'game', 'animals', 'are', 'ver...</td>\n",
       "      <td>0</td>\n",
       "      <td>{'neg': 0.0, 'neu': 1.0, 'pos': 0.0, 'compound...</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>{'neg': 0.0, 'neu': 1.0, 'pos': 0.0, 'compound...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.218052</td>\n",
       "      <td>0.012121</td>\n",
       "      <td>0.237727</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>157588222</td>\n",
       "      <td>english</td>\n",
       "      <td>game has been out for 4 months and they only a...</td>\n",
       "      <td>1707064205</td>\n",
       "      <td>1707064205</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>['new content', 'old level', 'social hub']</td>\n",
       "      <td>['game', 'has', 'been', 'months', 'only', 'add...</td>\n",
       "      <td>1</td>\n",
       "      <td>{'neg': 0.0, 'neu': 1.0, 'pos': 0.0, 'compound...</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>{'neg': 0.0, 'neu': 1.0, 'pos': 0.0, 'compound...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.116856</td>\n",
       "      <td>0.089899</td>\n",
       "      <td>-0.091288</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 34 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   recommendationid language  \\\n",
       "0         157603005  english   \n",
       "1         157602265  english   \n",
       "2         157598111  english   \n",
       "3         157592673  english   \n",
       "4         157588222  english   \n",
       "\n",
       "                                              review  timestamp_created  \\\n",
       "0  fun game to play when you've had a long day an...         1707077354   \n",
       "1  pros:\\nanimals\\nparty\\nfunny mechanics\\na bunc...         1707076676   \n",
       "2    10/10 would drop kick a cat without feeling bad         1707072837   \n",
       "3  very fun game. the animals are very cute, the ...         1707068019   \n",
       "4  game has been out for 4 months and they only a...         1707064205   \n",
       "\n",
       "   timestamp_updated  voted_up  votes_up  votes_funny  weighted_vote_score  \\\n",
       "0         1707077354      True         0            0                  0.0   \n",
       "1         1707076676      True         0            0                  0.0   \n",
       "2         1707072837      True         0            1                  0.0   \n",
       "3         1707068019      True         0            0                  0.0   \n",
       "4         1707064205     False         0            0                  0.0   \n",
       "\n",
       "   comment_count  ...                                attributes_patterns  \\\n",
       "0              0  ...              ['long day', 'fun easy', 'easy game']   \n",
       "1              0  ...  ['party funny', 'funny mechanics', 'short whil...   \n",
       "2              0  ...                                                 []   \n",
       "3              0  ...               ['fun game', 'new skins/characters']   \n",
       "4              0  ...         ['new content', 'old level', 'social hub']   \n",
       "\n",
       "                                             POS_all  cluster  \\\n",
       "0  ['fun', 'game', 'play', \"'ve\", 'had', 'long', ...        1   \n",
       "1  ['pros', 'animals', 'party', 'funny', 'mechani...        1   \n",
       "2          ['drop', 'kick', 'cat', 'feeling', 'bad']        1   \n",
       "3  ['very', 'fun', 'game', 'animals', 'are', 'ver...        0   \n",
       "4  ['game', 'has', 'been', 'months', 'only', 'add...        1   \n",
       "\n",
       "                     sentiment_scores_noun_pos_vader  \\\n",
       "0  {'neg': 0.0, 'neu': 0.392, 'pos': 0.608, 'comp...   \n",
       "1  {'neg': 0.0, 'neu': 0.854, 'pos': 0.146, 'comp...   \n",
       "2  {'neg': 0.0, 'neu': 1.0, 'pos': 0.0, 'compound...   \n",
       "3  {'neg': 0.0, 'neu': 1.0, 'pos': 0.0, 'compound...   \n",
       "4  {'neg': 0.0, 'neu': 1.0, 'pos': 0.0, 'compound...   \n",
       "\n",
       "   sentiment_score_noun_pos_vader  \\\n",
       "0                          0.7351   \n",
       "1                          0.4404   \n",
       "2                          0.0000   \n",
       "3                          0.0000   \n",
       "4                          0.0000   \n",
       "\n",
       "                      sentiment_scores_all_pos_vader  \\\n",
       "0  {'neg': 0.0, 'neu': 1.0, 'pos': 0.0, 'compound...   \n",
       "1  {'neg': 0.0, 'neu': 1.0, 'pos': 0.0, 'compound...   \n",
       "2  {'neg': 0.0, 'neu': 1.0, 'pos': 0.0, 'compound...   \n",
       "3  {'neg': 0.0, 'neu': 1.0, 'pos': 0.0, 'compound...   \n",
       "4  {'neg': 0.0, 'neu': 1.0, 'pos': 0.0, 'compound...   \n",
       "\n",
       "  sentiment_score_all_pos_vader  sentiment_score_review_spacy  \\\n",
       "0                           0.0                      0.031481   \n",
       "1                           0.0                     -0.000231   \n",
       "2                           0.0                     -0.700000   \n",
       "3                           0.0                      0.218052   \n",
       "4                           0.0                     -0.116856   \n",
       "\n",
       "   sentiment_score_noun_pos_spacy  sentiment_score_all_pos_spacy  \n",
       "0                        0.143333                       0.031481  \n",
       "1                        0.166667                      -0.011719  \n",
       "2                        0.000000                      -0.700000  \n",
       "3                        0.012121                       0.237727  \n",
       "4                        0.089899                      -0.091288  \n",
       "\n",
       "[5 rows x 34 columns]"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Perform sentiment analysis on the \"POS_all\" column, including Nouns, Verbs, Adjectives, Adverbs, etc.\n",
    "df['sentiment_score_all_pos_spacy'] = df['POS_all'].apply(lambda x: nlp(str(x))._.polarity)\n",
    "\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_test = df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Compare the sentiments scores with voted_up label, and set final sentiment score calculation approach."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>recommendationid</th>\n",
       "      <th>language</th>\n",
       "      <th>review</th>\n",
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       "      <td>fun game to play when you've had a long day an...</td>\n",
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       "      <td>0.031481</td>\n",
       "      <td>consistent</td>\n",
       "      <td>inconsistent</td>\n",
       "      <td>consistent</td>\n",
       "      <td>consistent</td>\n",
       "      <td>consistent</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>157602265</td>\n",
       "      <td>english</td>\n",
       "      <td>pros:\\nanimals\\nparty\\nfunny mechanics\\na bunc...</td>\n",
       "      <td>1707076676</td>\n",
       "      <td>1707076676</td>\n",
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       "      <td>-0.000231</td>\n",
       "      <td>0.166667</td>\n",
       "      <td>-0.011719</td>\n",
       "      <td>consistent</td>\n",
       "      <td>inconsistent</td>\n",
       "      <td>inconsistent</td>\n",
       "      <td>consistent</td>\n",
       "      <td>inconsistent</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>157598111</td>\n",
       "      <td>english</td>\n",
       "      <td>10/10 would drop kick a cat without feeling bad</td>\n",
       "      <td>1707072837</td>\n",
       "      <td>1707072837</td>\n",
       "      <td>True</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>{'neg': 0.0, 'neu': 1.0, 'pos': 0.0, 'compound...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.700000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-0.700000</td>\n",
       "      <td>inconsistent</td>\n",
       "      <td>inconsistent</td>\n",
       "      <td>inconsistent</td>\n",
       "      <td>inconsistent</td>\n",
       "      <td>inconsistent</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>157592673</td>\n",
       "      <td>english</td>\n",
       "      <td>very fun game. the animals are very cute, the ...</td>\n",
       "      <td>1707068019</td>\n",
       "      <td>1707068019</td>\n",
       "      <td>True</td>\n",
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       "      <td>0.218052</td>\n",
       "      <td>0.012121</td>\n",
       "      <td>0.237727</td>\n",
       "      <td>inconsistent</td>\n",
       "      <td>inconsistent</td>\n",
       "      <td>consistent</td>\n",
       "      <td>consistent</td>\n",
       "      <td>consistent</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>157588222</td>\n",
       "      <td>english</td>\n",
       "      <td>game has been out for 4 months and they only a...</td>\n",
       "      <td>1707064205</td>\n",
       "      <td>1707064205</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
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       "      <td>{'neg': 0.0, 'neu': 1.0, 'pos': 0.0, 'compound...</td>\n",
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       "      <td>-0.116856</td>\n",
       "      <td>0.089899</td>\n",
       "      <td>-0.091288</td>\n",
       "      <td>inconsistent</td>\n",
       "      <td>inconsistent</td>\n",
       "      <td>consistent</td>\n",
       "      <td>inconsistent</td>\n",
       "      <td>consistent</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 39 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   recommendationid language  \\\n",
       "0         157603005  english   \n",
       "1         157602265  english   \n",
       "2         157598111  english   \n",
       "3         157592673  english   \n",
       "4         157588222  english   \n",
       "\n",
       "                                              review  timestamp_created  \\\n",
       "0  fun game to play when you've had a long day an...         1707077354   \n",
       "1  pros:\\nanimals\\nparty\\nfunny mechanics\\na bunc...         1707076676   \n",
       "2    10/10 would drop kick a cat without feeling bad         1707072837   \n",
       "3  very fun game. the animals are very cute, the ...         1707068019   \n",
       "4  game has been out for 4 months and they only a...         1707064205   \n",
       "\n",
       "   timestamp_updated  voted_up  votes_up  votes_funny  weighted_vote_score  \\\n",
       "0         1707077354      True         0            0                  0.0   \n",
       "1         1707076676      True         0            0                  0.0   \n",
       "2         1707072837      True         0            1                  0.0   \n",
       "3         1707068019      True         0            0                  0.0   \n",
       "4         1707064205     False         0            0                  0.0   \n",
       "\n",
       "   comment_count  ...                     sentiment_scores_all_pos_vader  \\\n",
       "0              0  ...  {'neg': 0.0, 'neu': 1.0, 'pos': 0.0, 'compound...   \n",
       "1              0  ...  {'neg': 0.0, 'neu': 1.0, 'pos': 0.0, 'compound...   \n",
       "2              0  ...  {'neg': 0.0, 'neu': 1.0, 'pos': 0.0, 'compound...   \n",
       "3              0  ...  {'neg': 0.0, 'neu': 1.0, 'pos': 0.0, 'compound...   \n",
       "4              0  ...  {'neg': 0.0, 'neu': 1.0, 'pos': 0.0, 'compound...   \n",
       "\n",
       "   sentiment_score_all_pos_vader  sentiment_score_review_spacy  \\\n",
       "0                            0.0                      0.031481   \n",
       "1                            0.0                     -0.000231   \n",
       "2                            0.0                     -0.700000   \n",
       "3                            0.0                      0.218052   \n",
       "4                            0.0                     -0.116856   \n",
       "\n",
       "   sentiment_score_noun_pos_spacy  sentiment_score_all_pos_spacy  \\\n",
       "0                        0.143333                       0.031481   \n",
       "1                        0.166667                      -0.011719   \n",
       "2                        0.000000                      -0.700000   \n",
       "3                        0.012121                       0.237727   \n",
       "4                        0.089899                      -0.091288   \n",
       "\n",
       "   consistency_noun_pos_vader consistency_all_pos_vader  \\\n",
       "0                  consistent              inconsistent   \n",
       "1                  consistent              inconsistent   \n",
       "2                inconsistent              inconsistent   \n",
       "3                inconsistent              inconsistent   \n",
       "4                inconsistent              inconsistent   \n",
       "\n",
       "   consistency_review_spacy  consistency_noun_pos_spacy  \\\n",
       "0                consistent                  consistent   \n",
       "1              inconsistent                  consistent   \n",
       "2              inconsistent                inconsistent   \n",
       "3                consistent                  consistent   \n",
       "4                consistent                inconsistent   \n",
       "\n",
       "   consistency_all_pos_spacy  \n",
       "0                 consistent  \n",
       "1               inconsistent  \n",
       "2               inconsistent  \n",
       "3                 consistent  \n",
       "4                 consistent  \n",
       "\n",
       "[5 rows x 39 columns]"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create consistency columns for each sentiment score\n",
    "df_test['consistency_noun_pos_vader'] = df_test.apply(lambda row: 'consistent' if (row['sentiment_score_noun_pos_vader'] > 0 and row['voted_up']) or (row['sentiment_score_noun_pos_vader'] < 0 and not row['voted_up']) else 'inconsistent', axis=1)\n",
    "df_test['consistency_all_pos_vader'] = df_test.apply(lambda row: 'consistent' if (row['sentiment_score_all_pos_vader'] > 0 and row['voted_up']) or (row['sentiment_score_all_pos_vader'] < 0 and not row['voted_up']) else 'inconsistent', axis=1)\n",
    "df_test['consistency_review_spacy'] = df_test.apply(lambda row: 'consistent' if (row['sentiment_score_review_spacy'] > 0 and row['voted_up']) or (row['sentiment_score_review_spacy'] < 0 and not row['voted_up']) else 'inconsistent', axis=1)\n",
    "df_test['consistency_noun_pos_spacy'] = df_test.apply(lambda row: 'consistent' if (row['sentiment_score_noun_pos_spacy'] > 0 and row['voted_up']) or (row['sentiment_score_noun_pos_spacy'] <= 0 and not row['voted_up']) else 'inconsistent', axis=1)\n",
    "df_test['consistency_all_pos_spacy'] = df_test.apply(lambda row: 'consistent' if (row['sentiment_score_all_pos_spacy'] > 0 and row['voted_up']) or (row['sentiment_score_all_pos_spacy'] < 0 and not row['voted_up']) else 'inconsistent', axis=1)\n",
    "\n",
    "df_test.head()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Consistency Plot\n",
    "Winner is entire review calculated by SapCy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/tr/psvn_y8x0w34ssccnb237zq00000gn/T/ipykernel_3719/1995266060.py:4: FutureWarning: pandas.value_counts is deprecated and will be removed in a future version. Use pd.Series(obj).value_counts() instead.\n",
      "  Consistency_counts = df_test[['consistency_noun_pos_vader', 'consistency_all_pos_vader', 'consistency_review_spacy', 'consistency_noun_pos_spacy', 'consistency_all_pos_spacy']].apply(pd.value_counts)\n",
      "/var/folders/tr/psvn_y8x0w34ssccnb237zq00000gn/T/ipykernel_3719/1995266060.py:4: FutureWarning: pandas.value_counts is deprecated and will be removed in a future version. Use pd.Series(obj).value_counts() instead.\n",
      "  Consistency_counts = df_test[['consistency_noun_pos_vader', 'consistency_all_pos_vader', 'consistency_review_spacy', 'consistency_noun_pos_spacy', 'consistency_all_pos_spacy']].apply(pd.value_counts)\n",
      "/var/folders/tr/psvn_y8x0w34ssccnb237zq00000gn/T/ipykernel_3719/1995266060.py:4: FutureWarning: pandas.value_counts is deprecated and will be removed in a future version. Use pd.Series(obj).value_counts() instead.\n",
      "  Consistency_counts = df_test[['consistency_noun_pos_vader', 'consistency_all_pos_vader', 'consistency_review_spacy', 'consistency_noun_pos_spacy', 'consistency_all_pos_spacy']].apply(pd.value_counts)\n",
      "/var/folders/tr/psvn_y8x0w34ssccnb237zq00000gn/T/ipykernel_3719/1995266060.py:4: FutureWarning: pandas.value_counts is deprecated and will be removed in a future version. Use pd.Series(obj).value_counts() instead.\n",
      "  Consistency_counts = df_test[['consistency_noun_pos_vader', 'consistency_all_pos_vader', 'consistency_review_spacy', 'consistency_noun_pos_spacy', 'consistency_all_pos_spacy']].apply(pd.value_counts)\n",
      "/var/folders/tr/psvn_y8x0w34ssccnb237zq00000gn/T/ipykernel_3719/1995266060.py:4: FutureWarning: pandas.value_counts is deprecated and will be removed in a future version. Use pd.Series(obj).value_counts() instead.\n",
      "  Consistency_counts = df_test[['consistency_noun_pos_vader', 'consistency_all_pos_vader', 'consistency_review_spacy', 'consistency_noun_pos_spacy', 'consistency_all_pos_spacy']].apply(pd.value_counts)\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Count the inconsistencies\n",
    "Consistency_counts = df_test[['consistency_noun_pos_vader', 'consistency_all_pos_vader', 'consistency_review_spacy', 'consistency_noun_pos_spacy', 'consistency_all_pos_spacy']].apply(pd.value_counts)\n",
    "\n",
    "# Plot the inconsistencies\n",
    "Consistency_counts.plot(kind='bar')\n",
    "plt.xlabel('Consistency')\n",
    "plt.ylabel('Count')\n",
    "plt.title('Consistency Counts')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Calculate the accuracy rate of each sentiment analysis method \n",
    "\n",
    "consistent / column count: \n",
    "Columns inclduing: 'consistency_noun_pos_vader', 'consistency_all_pos_vader', 'consistency_review_spacy', 'consistency_noun_pos_spacy', 'consistency_all_pos_spacy'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Accuracy Rate</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>consistency_review_spacy</th>\n",
       "      <td>0.597766</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>consistency_all_pos_spacy</th>\n",
       "      <td>0.585936</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>consistency_noun_pos_spacy</th>\n",
       "      <td>0.374219</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>consistency_noun_pos_vader</th>\n",
       "      <td>0.123225</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>consistency_all_pos_vader</th>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                            Accuracy Rate\n",
       "consistency_review_spacy         0.597766\n",
       "consistency_all_pos_spacy        0.585936\n",
       "consistency_noun_pos_spacy       0.374219\n",
       "consistency_noun_pos_vader       0.123225\n",
       "consistency_all_pos_vader        0.000000"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Calculate the accuracy rate\n",
    "accuracy_rates = {\n",
    "    'consistency_noun_pos_vader': df_test['consistency_noun_pos_vader'].value_counts().get('consistent', 0) / len(df_test),\n",
    "    'consistency_all_pos_vader': df_test['consistency_all_pos_vader'].value_counts().get('consistent', 0) / len(df_test),\n",
    "    'consistency_review_spacy': df_test['consistency_review_spacy'].value_counts().get('consistent', 0) / len(df_test),\n",
    "    'consistency_noun_pos_spacy': df_test['consistency_noun_pos_spacy'].value_counts().get('consistent', 0) / len(df_test),\n",
    "    'consistency_all_pos_spacy': df_test['consistency_all_pos_spacy'].value_counts().get('consistent', 0) / len(df_test)\n",
    "}\n",
    "\n",
    "# Create a dataframe from the accuracy rates\n",
    "accuracy_df = pd.DataFrame.from_dict(accuracy_rates, orient='index', columns=['Accuracy Rate'])\n",
    "\n",
    "# Sort the dataframe in descending order\n",
    "accuracy_df = accuracy_df.sort_values(by='Accuracy Rate', ascending=False)\n",
    "\n",
    "accuracy_df\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Review + SpaCy Plots"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/tr/psvn_y8x0w34ssccnb237zq00000gn/T/ipykernel_3719/2863844937.py:11: FutureWarning: \n",
      "\n",
      "Passing `palette` without assigning `hue` is deprecated and will be removed in v0.14.0. Assign the `x` variable to `hue` and set `legend=False` for the same effect.\n",
      "\n",
      "  sns.boxplot(data=df_test, x='cluster', y='sentiment_score_review_spacy', ax=axes[0], palette=colors)\n",
      "/Users/kellyliu/anaconda3/lib/python3.11/site-packages/seaborn/_base.py:948: FutureWarning: When grouping with a length-1 list-like, you will need to pass a length-1 tuple to get_group in a future version of pandas. Pass `(name,)` instead of `name` to silence this warning.\n",
      "  data_subset = grouped_data.get_group(pd_key)\n",
      "/Users/kellyliu/anaconda3/lib/python3.11/site-packages/seaborn/categorical.py:632: FutureWarning: SeriesGroupBy.grouper is deprecated and will be removed in a future version of pandas.\n",
      "  positions = grouped.grouper.result_index.to_numpy(dtype=float)\n",
      "/Users/kellyliu/anaconda3/lib/python3.11/site-packages/seaborn/_base.py:948: FutureWarning: When grouping with a length-1 list-like, you will need to pass a length-1 tuple to get_group in a future version of pandas. Pass `(name,)` instead of `name` to silence this warning.\n",
      "  data_subset = grouped_data.get_group(pd_key)\n",
      "/Users/kellyliu/anaconda3/lib/python3.11/site-packages/seaborn/categorical.py:632: FutureWarning: SeriesGroupBy.grouper is deprecated and will be removed in a future version of pandas.\n",
      "  positions = grouped.grouper.result_index.to_numpy(dtype=float)\n",
      "/var/folders/tr/psvn_y8x0w34ssccnb237zq00000gn/T/ipykernel_3719/2863844937.py:15: FutureWarning: \n",
      "\n",
      "Passing `palette` without assigning `hue` is deprecated and will be removed in v0.14.0. Assign the `x` variable to `hue` and set `legend=False` for the same effect.\n",
      "\n",
      "  sns.violinplot(data=df_test, x='cluster', y='sentiment_score_review_spacy', ax=axes[1], palette=colors)\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1200x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "# Set the colors for the segments\n",
    "colors = ['#77aa83', '#e6902e']\n",
    "\n",
    "# Create a figure with subplots\n",
    "fig, axes = plt.subplots(1, 2, figsize=(12, 6))\n",
    "\n",
    "# Boxplot\n",
    "sns.boxplot(data=df_test, x='cluster', y='sentiment_score_review_spacy', ax=axes[0], palette=colors)\n",
    "axes[0].set_title('Boxplot')\n",
    "\n",
    "# Violin plot\n",
    "sns.violinplot(data=df_test, x='cluster', y='sentiment_score_review_spacy', ax=axes[1], palette=colors)\n",
    "axes[1].set_title('Violin Plot')\n",
    "\n",
    "# Adjust the layout\n",
    "plt.tight_layout()\n",
    "\n",
    "# Show the plots\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# All POS + SpaCy Plots"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/tr/psvn_y8x0w34ssccnb237zq00000gn/T/ipykernel_3719/4171637881.py:11: FutureWarning: \n",
      "\n",
      "Passing `palette` without assigning `hue` is deprecated and will be removed in v0.14.0. Assign the `x` variable to `hue` and set `legend=False` for the same effect.\n",
      "\n",
      "  sns.boxplot(data=df_test, x='cluster', y='sentiment_score_all_pos_spacy', ax=axes[0], palette=colors)\n",
      "/Users/kellyliu/anaconda3/lib/python3.11/site-packages/seaborn/_base.py:948: FutureWarning: When grouping with a length-1 list-like, you will need to pass a length-1 tuple to get_group in a future version of pandas. Pass `(name,)` instead of `name` to silence this warning.\n",
      "  data_subset = grouped_data.get_group(pd_key)\n",
      "/Users/kellyliu/anaconda3/lib/python3.11/site-packages/seaborn/categorical.py:632: FutureWarning: SeriesGroupBy.grouper is deprecated and will be removed in a future version of pandas.\n",
      "  positions = grouped.grouper.result_index.to_numpy(dtype=float)\n",
      "/Users/kellyliu/anaconda3/lib/python3.11/site-packages/seaborn/_base.py:948: FutureWarning: When grouping with a length-1 list-like, you will need to pass a length-1 tuple to get_group in a future version of pandas. Pass `(name,)` instead of `name` to silence this warning.\n",
      "  data_subset = grouped_data.get_group(pd_key)\n",
      "/Users/kellyliu/anaconda3/lib/python3.11/site-packages/seaborn/categorical.py:632: FutureWarning: SeriesGroupBy.grouper is deprecated and will be removed in a future version of pandas.\n",
      "  positions = grouped.grouper.result_index.to_numpy(dtype=float)\n",
      "/var/folders/tr/psvn_y8x0w34ssccnb237zq00000gn/T/ipykernel_3719/4171637881.py:15: FutureWarning: \n",
      "\n",
      "Passing `palette` without assigning `hue` is deprecated and will be removed in v0.14.0. Assign the `x` variable to `hue` and set `legend=False` for the same effect.\n",
      "\n",
      "  sns.violinplot(data=df_test, x='cluster', y='sentiment_score_all_pos_spacy', ax=axes[1], palette=colors)\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1200x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "# Set the colors for the segments\n",
    "colors = ['#77aa83', '#e6902e']\n",
    "\n",
    "# Create a figure with subplots\n",
    "fig, axes = plt.subplots(1, 2, figsize=(12, 6))\n",
    "\n",
    "# Boxplot\n",
    "sns.boxplot(data=df_test, x='cluster', y='sentiment_score_all_pos_spacy', ax=axes[0], palette=colors)\n",
    "axes[0].set_title('Boxplot')\n",
    "\n",
    "# Violin plot\n",
    "sns.violinplot(data=df_test, x='cluster', y='sentiment_score_all_pos_spacy', ax=axes[1], palette=colors)\n",
    "axes[1].set_title('Violin Plot')\n",
    "\n",
    "# Adjust the layout\n",
    "plt.tight_layout()\n",
    "\n",
    "# Show the plots\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.to_csv('Vader vs. Spacy Sentiments Scores.csv', index=False)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Split the CSV into two segments based on the cluster column\n",
    "segment_0 = df[df['cluster'] == 0]\n",
    "segment_1 = df[df['cluster'] == 1]\n",
    "\n",
    "# Output the segments as separate CSV files\n",
    "segment_0.to_csv('segment_0.csv', index=False)\n",
    "segment_1.to_csv('segment_1.csv', index=False)\n"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "base",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}