NLP_Basics_2.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### TfidVectorizer --"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "# TfidVectorizer - Converting the word into numerical values vectors so that it can receive or given to the machine Learning model.\n",
    "from sklearn.feature_extraction.text import TfidfVectorizer\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "A1 = \"hello and welcome dosto\"\n",
    "A2 = 'shri love NLP'\n",
    "A3 = 'shri is good boy'\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "        boy    dosto      good    hello      love       nlp     shri  welcome\n",
      "0  0.000000  0.57735  0.000000  0.57735  0.000000  0.000000  0.00000  0.57735\n",
      "1  0.000000  0.00000  0.000000  0.00000  0.622766  0.622766  0.47363  0.00000\n",
      "2  0.622766  0.00000  0.622766  0.00000  0.000000  0.000000  0.47363  0.00000\n"
     ]
    }
   ],
   "source": [
    "vectorizer = TfidfVectorizer(stop_words='english')\n",
    "vectors = vectorizer.fit_transform([A1,A2,A3])\n",
    "feature_names = vectorizer.get_feature_names_out()\n",
    "dense = vectors.todense()\n",
    "result = pd.DataFrame(dense,columns=feature_names)\n",
    "print(result)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### word2vecfinal -- "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: gensim in c:\\users\\user\\miniconda3\\lib\\site-packages (4.3.2)\n",
      "Requirement already satisfied: smart-open>=1.8.1 in c:\\users\\user\\miniconda3\\lib\\site-packages (from gensim) (7.0.4)\n",
      "Requirement already satisfied: numpy>=1.18.5 in c:\\users\\user\\miniconda3\\lib\\site-packages (from gensim) (1.25.0)\n",
      "Requirement already satisfied: scipy>=1.7.0 in c:\\users\\user\\miniconda3\\lib\\site-packages (from gensim) (1.11.1)\n",
      "Requirement already satisfied: wrapt in c:\\users\\user\\miniconda3\\lib\\site-packages (from smart-open>=1.8.1->gensim) (1.16.0)\n",
      "Note: you may need to restart the kernel to use updated packages.\n"
     ]
    }
   ],
   "source": [
    "pip install gensim"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "from nltk.stem import WordNetLemmatizer\n",
    "lemmatizer = WordNetLemmatizer()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "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>review</th>\n",
       "      <th>label</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>loved the movie action, best performance by ev...</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>It was a good movie, a blockbuster cinema</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>I loved the movie and acting</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>best part was acting, awesome performance</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Total waste of money</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Poor storyline, and very bad performance by th...</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>I Give 9/10 for this one.9/10 because,Editing ...</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>First half is very slow and boring</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Songs are the best part of movie, I liked it</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>such a waste of time and money, rubbish movie</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>I will give it a big zero, what a waste</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Total overacting by the leads</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>No doubt. This film is the changing face for f...</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>The best of all Indian movies.Nothing can be c...</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>movie is known for his suspenses and thriller....</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Good Story ,Great Visuals,best part is there a...</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Worst movie of the  decade. i am giving 1 star...</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>A very illogical action sequences as usual.</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>Remarkable direction, outstanding acting, monu...</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>Meaningless, in appropriate, boring!!!</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>This is undoubtedly one of the worst big budge...</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>This was 3rd class acting and screen work. I w...</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>The worst movie I've ever seen in my whole lif...</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>An epic milestone in film industry!! I am very...</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>Fabulous work done by lead,we can't get bored ...</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>this movie is a win for the whole film industry.</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>the performances was very average, actors lite...</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>this movie is ridiculous,very BAD BAD BAD BAD</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>garbage movie, 0 out of 5</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>performance is mindblowing. The depiction of a...</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>fantastic movie writting, best performance by ...</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>superb flop movie i ever seen in my life loser...</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>only stunt is not important acting is also req...</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>This movie is a very horrible and a worst movi...</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>lead acting was like a fool, rubbish story, ve...</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>Game Changer, Mass Cinematic Brilliance</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>It was a mind-blowing experience to watch such...</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>literally the background movie gave me goosebu...</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>this movie should be banned, very disrespectfull</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>This movie is a masterpiece with a unique stor...</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>This is the worst movie I've ever seen in my w...</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>ONE OF THE MOST POWERFUL MOVIES I HAVE EVER WA...</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>This movie just is a disaster,weak narrative a...</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>Excellent Story, best screen play,great  direc...</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>Very very flop movie.worst worst worst film in...</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>The best Indian action film,absolutely brillia...</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>this movie is one of my favorites, engaging mu...</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>The action was so unnecessary ,dialoges were v...</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>It is the best best best great movie I have ev...</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>This movie is a very horrible and a worst movi...</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                               review  label\n",
       "0   loved the movie action, best performance by ev...      1\n",
       "1           It was a good movie, a blockbuster cinema      1\n",
       "2                        I loved the movie and acting      1\n",
       "3           best part was acting, awesome performance      1\n",
       "4                                Total waste of money      0\n",
       "5   Poor storyline, and very bad performance by th...      0\n",
       "6   I Give 9/10 for this one.9/10 because,Editing ...      1\n",
       "7                  First half is very slow and boring      0\n",
       "8        Songs are the best part of movie, I liked it      1\n",
       "9       such a waste of time and money, rubbish movie      0\n",
       "10            I will give it a big zero, what a waste      0\n",
       "11                      Total overacting by the leads      0\n",
       "12  No doubt. This film is the changing face for f...      1\n",
       "13  The best of all Indian movies.Nothing can be c...      1\n",
       "14  movie is known for his suspenses and thriller....      0\n",
       "15  Good Story ,Great Visuals,best part is there a...      1\n",
       "16  Worst movie of the  decade. i am giving 1 star...      0\n",
       "17        A very illogical action sequences as usual.      0\n",
       "18  Remarkable direction, outstanding acting, monu...      1\n",
       "19             Meaningless, in appropriate, boring!!!      0\n",
       "20  This is undoubtedly one of the worst big budge...      0\n",
       "21  This was 3rd class acting and screen work. I w...      0\n",
       "22  The worst movie I've ever seen in my whole lif...      0\n",
       "23  An epic milestone in film industry!! I am very...      1\n",
       "24  Fabulous work done by lead,we can't get bored ...      1\n",
       "25   this movie is a win for the whole film industry.      1\n",
       "26  the performances was very average, actors lite...      0\n",
       "27      this movie is ridiculous,very BAD BAD BAD BAD      0\n",
       "28                          garbage movie, 0 out of 5      0\n",
       "29  performance is mindblowing. The depiction of a...      1\n",
       "30  fantastic movie writting, best performance by ...      1\n",
       "31  superb flop movie i ever seen in my life loser...      0\n",
       "32  only stunt is not important acting is also req...      0\n",
       "33  This movie is a very horrible and a worst movi...      0\n",
       "34  lead acting was like a fool, rubbish story, ve...      0\n",
       "35            Game Changer, Mass Cinematic Brilliance      1\n",
       "36  It was a mind-blowing experience to watch such...      1\n",
       "37  literally the background movie gave me goosebu...      1\n",
       "38   this movie should be banned, very disrespectfull      0\n",
       "39  This movie is a masterpiece with a unique stor...      1\n",
       "40  This is the worst movie I've ever seen in my w...      0\n",
       "41  ONE OF THE MOST POWERFUL MOVIES I HAVE EVER WA...      1\n",
       "42  This movie just is a disaster,weak narrative a...      0\n",
       "43  Excellent Story, best screen play,great  direc...      1\n",
       "44  Very very flop movie.worst worst worst film in...      0\n",
       "45  The best Indian action film,absolutely brillia...      1\n",
       "46  this movie is one of my favorites, engaging mu...      1\n",
       "47  The action was so unnecessary ,dialoges were v...      0\n",
       "48  It is the best best best great movie I have ev...      1\n",
       "49  This movie is a very horrible and a worst movi...      0"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset = pd.read_csv('finalreview.csv')\n",
    "dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Basic Pre-Processing -\n",
    "from nltk.corpus import stopwords\n",
    "import re\n",
    "corpus = []\n",
    "for i in range(0, 29):\n",
    "    review = re.sub('[^a-zA-Z]', ' ', dataset['review'][i])\n",
    "    review = review.lower()\n",
    "    review = review.split() \n",
    "    review = [lemmatizer.lemmatize(word) for word in review if not word in stopwords.words('english')]\n",
    "    review = ' '.join(review)\n",
    "    corpus.append(review)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['loved movie action best performance every actor',\n",
       " 'good movie blockbuster cinema',\n",
       " 'loved movie acting',\n",
       " 'best part acting awesome performance',\n",
       " 'total waste money',\n",
       " 'poor storyline bad performance lead',\n",
       " 'give one editing much fast except everything felt right place',\n",
       " 'first half slow boring',\n",
       " 'song best part movie liked',\n",
       " 'waste time money rubbish movie',\n",
       " 'give big zero waste',\n",
       " 'total overacting lead',\n",
       " 'doubt film changing face film industry every people put hard work effort',\n",
       " 'best indian movie nothing compared',\n",
       " 'movie known suspense thriller missing film really bad',\n",
       " 'good story great visuals best part many character film really increase curiosity audience acting awesome',\n",
       " 'worst movie decade giving star',\n",
       " 'illogical action sequence usual',\n",
       " 'remarkable direction outstanding acting monumental narration unthinkable screenplay striking action sequence',\n",
       " 'meaningless appropriate boring',\n",
       " 'undoubtedly one worst big budget commercial movie seen',\n",
       " 'rd class acting screen work extremely disappointed',\n",
       " 'worst movie ever seen whole life boring nonsense',\n",
       " 'epic milestone film industry proud indian great movie best time',\n",
       " 'fabulous work done lead get bored watching movie screenplay background music cinematography amazing',\n",
       " 'movie win whole film industry',\n",
       " 'performance average actor literally walked talked acted like robot',\n",
       " 'movie ridiculous bad bad bad bad',\n",
       " 'garbage movie']"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "corpus"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Another step of Basic Pre-processing into tokens -(Best way)\n",
    "from nltk import sent_tokenize\n",
    "from gensim.utils import simple_preprocess\n",
    "\n",
    "sentences = []\n",
    "for sent in corpus:\n",
    "    sent_token=sent_tokenize(sent)\n",
    "    for sent in sent_token:\n",
    "        sentences.append(simple_preprocess(sent))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "import gensim # Making tokens using gensim\n",
    "model = gensim.models.Word2Vec(sentences,window=3,min_count=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['movie',\n",
       " 'best',\n",
       " 'bad',\n",
       " 'film',\n",
       " 'acting',\n",
       " 'performance',\n",
       " 'part',\n",
       " 'lead',\n",
       " 'waste',\n",
       " 'industry',\n",
       " 'boring',\n",
       " 'worst',\n",
       " 'work',\n",
       " 'action',\n",
       " 'whole',\n",
       " 'indian',\n",
       " 'great',\n",
       " 'sequence',\n",
       " 'big',\n",
       " 'time',\n",
       " 'really',\n",
       " 'screenplay',\n",
       " 'loved',\n",
       " 'seen',\n",
       " 'money',\n",
       " 'one',\n",
       " 'total',\n",
       " 'every',\n",
       " 'actor',\n",
       " 'good',\n",
       " 'awesome',\n",
       " 'give',\n",
       " 'changing',\n",
       " 'face',\n",
       " 'cinema',\n",
       " 'editing',\n",
       " 'people',\n",
       " 'doubt',\n",
       " 'hard',\n",
       " 'blockbuster',\n",
       " 'effort',\n",
       " 'nothing',\n",
       " 'compared',\n",
       " 'put',\n",
       " 'zero',\n",
       " 'overacting',\n",
       " 'much',\n",
       " 'fast',\n",
       " 'except',\n",
       " 'everything',\n",
       " 'felt',\n",
       " 'right',\n",
       " 'suspense',\n",
       " 'place',\n",
       " 'first',\n",
       " 'half',\n",
       " 'slow',\n",
       " 'song',\n",
       " 'liked',\n",
       " 'storyline',\n",
       " 'rubbish',\n",
       " 'poor',\n",
       " 'known',\n",
       " 'garbage',\n",
       " 'thriller',\n",
       " 'watching',\n",
       " 'disappointed',\n",
       " 'ever',\n",
       " 'life',\n",
       " 'nonsense',\n",
       " 'epic',\n",
       " 'milestone',\n",
       " 'proud',\n",
       " 'fabulous',\n",
       " 'done',\n",
       " 'get',\n",
       " 'bored',\n",
       " 'background',\n",
       " 'screen',\n",
       " 'music',\n",
       " 'cinematography',\n",
       " 'amazing',\n",
       " 'win',\n",
       " 'average',\n",
       " 'literally',\n",
       " 'walked',\n",
       " 'talked',\n",
       " 'acted',\n",
       " 'like',\n",
       " 'robot',\n",
       " 'extremely',\n",
       " 'class',\n",
       " 'missing',\n",
       " 'usual',\n",
       " 'ridiculous',\n",
       " 'visuals',\n",
       " 'many',\n",
       " 'character',\n",
       " 'increase',\n",
       " 'curiosity',\n",
       " 'audience',\n",
       " 'decade',\n",
       " 'giving',\n",
       " 'star',\n",
       " 'illogical',\n",
       " 'remarkable',\n",
       " 'rd',\n",
       " 'direction',\n",
       " 'outstanding',\n",
       " 'monumental',\n",
       " 'narration',\n",
       " 'unthinkable',\n",
       " 'striking',\n",
       " 'meaningless',\n",
       " 'appropriate',\n",
       " 'undoubtedly',\n",
       " 'budget',\n",
       " 'commercial',\n",
       " 'story']"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.wv.index_to_key"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "from gensim.models import KeyedVectors\n",
    "word_vectors = model.wv\n",
    "word_vectors.save(\"word2vec.wordvectors\")\n",
    "\n",
    "wv = KeyedVectors.load(\"word2vec.wordvectors\") # saving the word vector file\n",
    "vector = wv['worst']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 9.7802887e-03,  8.1691816e-03,  1.2762035e-03,  5.0976058e-03,\n",
       "        1.4070637e-03, -6.4661731e-03, -1.4169702e-03,  6.4737783e-03,\n",
       "       -4.6283016e-03, -3.9985883e-03,  4.9228244e-03,  2.7162295e-03,\n",
       "       -1.8412578e-03, -2.8709208e-03,  5.9995907e-03, -5.7205064e-03,\n",
       "       -3.2234399e-03, -6.4902911e-03, -4.2465297e-03, -8.5991677e-03,\n",
       "       -4.4568083e-03, -8.5190870e-03,  1.4122641e-03, -8.6348727e-03,\n",
       "       -9.9129276e-03, -8.2063852e-03, -6.7810114e-03,  6.6846199e-03,\n",
       "        3.7707100e-03,  3.6329692e-04, -2.9542963e-03, -7.4233352e-03,\n",
       "        5.3763355e-04,  4.8847735e-04,  1.9537541e-04,  8.5930963e-04,\n",
       "        7.8601495e-04, -7.1654154e-05, -8.0049559e-03, -5.8858236e-03,\n",
       "       -8.3860680e-03, -1.3135688e-03,  1.8097514e-03,  7.4178926e-03,\n",
       "       -1.9480338e-03, -2.3379966e-03,  9.4815632e-03,  7.8400117e-05,\n",
       "       -2.3969549e-03,  8.6077750e-03,  2.6818896e-03, -5.3472142e-03,\n",
       "        6.5865959e-03,  4.4976361e-03, -7.0453193e-03, -3.2886473e-04,\n",
       "        8.3362864e-04,  5.7486845e-03, -1.7315275e-03, -2.8049788e-03,\n",
       "        1.7589382e-03,  8.3503453e-04,  1.2025072e-03, -2.6366934e-03,\n",
       "       -5.9847217e-03,  7.3362477e-03,  7.5955209e-03,  8.3090551e-03,\n",
       "       -8.6135929e-03,  2.6418150e-03, -3.5637422e-03,  9.6234791e-03,\n",
       "        2.9114657e-03,  4.6392959e-03,  2.3891600e-03,  6.6080410e-03,\n",
       "       -5.7369219e-03,  7.8936480e-03, -2.4171355e-03, -4.5677540e-03,\n",
       "       -2.0624208e-03,  9.7365556e-03, -6.8632388e-03, -2.1749551e-03,\n",
       "        7.0018750e-03, -6.2717474e-05, -6.2750969e-03, -6.3900114e-03,\n",
       "        8.9458646e-03,  6.4379396e-03,  4.7905645e-03, -3.2493395e-03,\n",
       "       -9.2608016e-03,  3.7830963e-03,  7.1844915e-03, -5.6325225e-03,\n",
       "       -7.8729866e-03, -2.9694727e-03, -4.9211653e-03, -2.3217679e-03],\n",
       "      dtype=float32)"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vector"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([-5.3341099e-04,  2.4877596e-04,  5.1067229e-03,  9.0310713e-03,\n",
       "       -9.3028788e-03, -7.1458262e-03,  6.4698206e-03,  9.0199215e-03,\n",
       "       -5.0632064e-03, -3.7538598e-03,  7.3775849e-03, -1.5443944e-03,\n",
       "       -4.5120013e-03,  6.5862411e-03, -4.8559140e-03, -1.8092741e-03,\n",
       "        2.8728831e-03,  9.8923687e-04, -8.3175460e-03, -9.4934739e-03,\n",
       "        7.3423120e-03,  5.0478713e-03,  6.7715994e-03,  7.2577427e-04,\n",
       "        6.3490258e-03, -3.4283239e-03, -9.8952313e-04,  5.7464736e-03,\n",
       "       -7.5430633e-03, -3.9668255e-03, -7.4898889e-03, -9.0812804e-04,\n",
       "        9.5868083e-03, -7.3320772e-03, -2.3491837e-03, -1.9063272e-03,\n",
       "        8.0650076e-03, -5.9145698e-03,  4.1875726e-05, -4.7986852e-03,\n",
       "       -9.6011078e-03,  5.0092358e-03, -8.7747844e-03, -4.3919501e-03,\n",
       "       -3.9211223e-05, -3.3092278e-04, -7.6658777e-03,  9.6039977e-03,\n",
       "        4.9739932e-03,  9.2564505e-03, -8.1743533e-03,  4.4893669e-03,\n",
       "       -4.1501140e-03,  7.9350447e-04,  8.5513070e-03, -4.5030508e-03,\n",
       "        4.5256675e-03, -6.7721368e-03, -3.5695583e-03,  9.3986867e-03,\n",
       "       -1.5592007e-03,  3.1528700e-04, -4.1040615e-03, -7.6836725e-03,\n",
       "       -1.5478695e-03,  2.4792738e-03, -8.7300461e-04,  5.5526970e-03,\n",
       "       -2.7857597e-03,  2.2987477e-03,  5.4297382e-03,  8.3398372e-03,\n",
       "       -1.4616498e-03, -9.2089614e-03,  4.4144830e-03,  5.4533937e-04,\n",
       "        7.4163647e-03, -8.2172145e-04, -2.6621292e-03, -8.7549174e-03,\n",
       "       -8.5223047e-04,  2.8266271e-03,  5.3863712e-03,  7.0932992e-03,\n",
       "       -5.7354802e-03,  1.8554602e-03,  6.1396579e-03, -4.7874483e-03,\n",
       "       -3.0905979e-03,  6.8194061e-03,  1.6431520e-03,  2.0280528e-04,\n",
       "        3.5078437e-03,  2.2863108e-04,  9.6819820e-03,  5.0689303e-03,\n",
       "       -8.9435680e-03, -7.0165019e-03,  9.1766799e-04,  6.3675898e-03],\n",
       "      dtype=float32)"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# default vector size = 100\n",
    "wv['movie']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 1.2923613e-03, -9.7971987e-03,  4.5714914e-03, -5.1193731e-04,\n",
       "        6.3125924e-03,  1.7667388e-03, -3.1173185e-03,  7.8051090e-03,\n",
       "        1.5226129e-03,  4.3091608e-05, -4.6093585e-03, -8.4702531e-03,\n",
       "       -7.7684894e-03,  8.6929509e-03, -8.9200567e-03,  9.0421522e-03,\n",
       "       -9.2776166e-03, -2.7083728e-04, -1.9206842e-03, -8.9661228e-03,\n",
       "        8.6443573e-03,  6.7567420e-03,  3.0339879e-03,  4.8114071e-03,\n",
       "        1.2912856e-04,  9.4206631e-03,  7.0081679e-03, -9.8710321e-03,\n",
       "       -4.4466816e-03, -1.3150929e-03,  3.0609439e-03, -4.3218760e-03,\n",
       "        1.4747735e-03, -7.8537660e-03,  2.7754903e-03,  4.7101504e-03,\n",
       "        4.9491636e-03, -3.1787534e-03, -8.4213382e-03, -9.2458623e-03,\n",
       "       -7.2274607e-04, -7.3336274e-03, -6.8333233e-03,  6.1170771e-03,\n",
       "        7.1818042e-03,  2.0813837e-03, -7.9199551e-03, -5.6905122e-03,\n",
       "        8.0638761e-03,  3.9371871e-03, -5.2492614e-03, -7.3872320e-03,\n",
       "        7.5327558e-04,  3.4531318e-03,  2.1022870e-03,  3.0823813e-03,\n",
       "       -5.6118947e-03, -9.8939873e-03, -7.0425947e-03,  2.2984682e-04,\n",
       "        4.6300250e-03,  4.5529325e-03,  1.9042337e-03,  5.1647769e-03,\n",
       "       -1.2611164e-04,  4.1156630e-03, -9.1169076e-03,  7.7431779e-03,\n",
       "        6.1267191e-03,  5.1495889e-03,  7.2060744e-03,  8.4415032e-03,\n",
       "        7.3111145e-04, -1.7031553e-03,  5.5606879e-04, -9.3177129e-03,\n",
       "        8.3943503e-03, -6.3762409e-03,  8.4245708e-03, -4.2484966e-03,\n",
       "        6.4398983e-04, -9.1885719e-03, -9.5591210e-03, -7.7972757e-03,\n",
       "       -7.7434219e-03,  3.7238226e-04, -7.2024139e-03, -4.9422318e-03,\n",
       "       -5.2582957e-03, -4.2797001e-03,  7.0213312e-03,  4.8562516e-03,\n",
       "        8.7173497e-03,  7.1055479e-03, -5.6458833e-03,  7.2528343e-03,\n",
       "       -9.2981244e-03, -2.5872246e-03, -7.7304984e-03,  4.1957833e-03],\n",
       "      dtype=float32)"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wv['great']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('proud', 0.29040318727493286),\n",
       " ('many', 0.23207567632198334),\n",
       " ('great', 0.22010107338428497),\n",
       " ('industry', 0.21900497376918793),\n",
       " ('watching', 0.20550432801246643),\n",
       " ('actor', 0.19623328745365143),\n",
       " ('place', 0.17265573143959045),\n",
       " ('put', 0.17074155807495117),\n",
       " ('awesome', 0.15295808017253876),\n",
       " ('cinema', 0.14276576042175293)]"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wv.similar_by_word('movie') # vector value similar with the word given by the user"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "def sentence_vector(sent):\n",
    "    doc = [word for word in sent.split() if word in model.wv.index_to_key]\n",
    "    return np.mean(model.wv[doc], axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0.00422246, -0.0008309 ,  0.00155276,  0.00049586, -0.00167588,\n",
       "        0.00012432,  0.00054   ,  0.00472425, -0.00258039, -0.00141265,\n",
       "       -0.00065444,  0.00072133, -0.00094032,  0.00035494,  0.0006083 ,\n",
       "        0.00081437,  0.00107797, -0.00161981,  0.001287  , -0.00065216,\n",
       "        0.00038056, -0.00222888, -0.00123109, -0.00538537, -0.00221802,\n",
       "       -0.00137051, -0.00424015,  0.00056689,  0.00199476, -0.00040052,\n",
       "        0.00512448, -0.00135929, -0.00306951, -0.00178853, -0.00118769,\n",
       "        0.00161797,  0.00322041, -0.00207535, -0.00412099, -0.00260661,\n",
       "        0.00177594, -0.00053891,  0.00425165,  0.00152007, -0.00187223,\n",
       "       -0.00189343, -0.00133834, -0.00140753,  0.00288288, -0.00042716,\n",
       "        0.0013594 ,  0.00101374,  0.00133179,  0.00122224, -0.0007652 ,\n",
       "        0.00293508, -0.00217569,  0.00295454, -0.00077916, -0.00118152,\n",
       "       -0.00103254,  0.00247701,  0.00262405,  0.00285418, -0.00213168,\n",
       "        0.00382419,  0.00380276, -0.00031127, -0.00196237,  0.00220859,\n",
       "       -0.00328509,  0.00326415, -0.00082635,  0.00214542,  0.00232354,\n",
       "       -0.00043942, -0.00255671,  0.00244501, -0.00265616,  0.00175954,\n",
       "       -0.00231958,  0.00355385,  0.00041914,  0.00478816, -0.00053459,\n",
       "        0.0016274 , -0.00157844,  0.00272899, -0.00091207,  0.00186942,\n",
       "       -0.00083183,  0.00062234, -0.00170373, -0.00068953,  0.00213106,\n",
       "        0.00020783, -0.00130983, -0.00032026, -0.00038107,  0.00155654],\n",
       "      dtype=float32)"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sentence_vector(dataset['review'].values[20]) # index number 20 is send to sentence_vector"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "from tqdm import tqdm # This library is used to check the progress of the task"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 38%|███▊      | 19/50 [00:00<00:00, 730.17it/s]\n"
     ]
    },
    {
     "ename": "ValueError",
     "evalue": "need at least one array to concatenate",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "Cell \u001b[1;32mIn[32], line 3\u001b[0m\n\u001b[0;32m      1\u001b[0m final \u001b[38;5;241m=\u001b[39m []\n\u001b[0;32m      2\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m s \u001b[38;5;129;01min\u001b[39;00m tqdm(dataset[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mreview\u001b[39m\u001b[38;5;124m'\u001b[39m]\u001b[38;5;241m.\u001b[39mvalues):\n\u001b[1;32m----> 3\u001b[0m     final\u001b[38;5;241m.\u001b[39mappend(\u001b[43msentence_vector\u001b[49m\u001b[43m(\u001b[49m\u001b[43ms\u001b[49m\u001b[43m)\u001b[49m)\n",
      "Cell \u001b[1;32mIn[28], line 4\u001b[0m, in \u001b[0;36msentence_vector\u001b[1;34m(sent)\u001b[0m\n\u001b[0;32m      2\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21msentence_vector\u001b[39m(sent):\n\u001b[0;32m      3\u001b[0m     doc \u001b[38;5;241m=\u001b[39m [word \u001b[38;5;28;01mfor\u001b[39;00m word \u001b[38;5;129;01min\u001b[39;00m sent\u001b[38;5;241m.\u001b[39msplit() \u001b[38;5;28;01mif\u001b[39;00m word \u001b[38;5;129;01min\u001b[39;00m model\u001b[38;5;241m.\u001b[39mwv\u001b[38;5;241m.\u001b[39mindex_to_key]\n\u001b[1;32m----> 4\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m np\u001b[38;5;241m.\u001b[39mmean(\u001b[43mmodel\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mwv\u001b[49m\u001b[43m[\u001b[49m\u001b[43mdoc\u001b[49m\u001b[43m]\u001b[49m, axis\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m0\u001b[39m)\n",
      "File \u001b[1;32mc:\\Users\\user\\miniconda3\\lib\\site-packages\\gensim\\models\\keyedvectors.py:405\u001b[0m, in \u001b[0;36mKeyedVectors.__getitem__\u001b[1;34m(self, key_or_keys)\u001b[0m\n\u001b[0;32m    402\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(key_or_keys, _KEY_TYPES):\n\u001b[0;32m    403\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mget_vector(key_or_keys)\n\u001b[1;32m--> 405\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mvstack\u001b[49m\u001b[43m(\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget_vector\u001b[49m\u001b[43m(\u001b[49m\u001b[43mkey\u001b[49m\u001b[43m)\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mfor\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mkey\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;129;43;01min\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mkey_or_keys\u001b[49m\u001b[43m]\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[1;32mc:\\Users\\user\\miniconda3\\lib\\site-packages\\numpy\\core\\shape_base.py:289\u001b[0m, in \u001b[0;36mvstack\u001b[1;34m(tup, dtype, casting)\u001b[0m\n\u001b[0;32m    287\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(arrs, \u001b[38;5;28mlist\u001b[39m):\n\u001b[0;32m    288\u001b[0m     arrs \u001b[38;5;241m=\u001b[39m [arrs]\n\u001b[1;32m--> 289\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43m_nx\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mconcatenate\u001b[49m\u001b[43m(\u001b[49m\u001b[43marrs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m0\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdtype\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdtype\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcasting\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcasting\u001b[49m\u001b[43m)\u001b[49m\n",
      "\u001b[1;31mValueError\u001b[0m: need at least one array to concatenate"
     ]
    }
   ],
   "source": [
    "final = []\n",
    "for s in tqdm(dataset['review'].values):\n",
    "    final.append(sentence_vector(s))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
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       "       dtype=float32),\n",
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       "         6.0503925e-03,  8.0573838e-05,  5.8427416e-03, -7.5136609e-03],\n",
       "       dtype=float32),\n",
       " array([-0.00430405,  0.00261394, -0.00383708, -0.00534103, -0.00086543,\n",
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       "        -5.9708068e-04,  4.9621810e-04, -9.7923575e-04,  2.7176111e-03,\n",
       "         3.2730955e-03,  4.8244568e-03,  1.8814623e-03,  2.3779236e-03],\n",
       "       dtype=float32)]"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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   "language": "python",
   "name": "python3"
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   "codemirror_mode": {
    "name": "ipython",
    "version": 3
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