sentiment_analysis.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import os\n",
    "import joblib\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\n",
       "    .dataframe thead th {\n",
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       "    }\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>sentiment</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>One of the other reviewers has mentioned that ...</td>\n",
       "      <td>positive</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>A wonderful little production. &lt;br /&gt;&lt;br /&gt;The...</td>\n",
       "      <td>positive</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>I thought this was a wonderful way to spend ti...</td>\n",
       "      <td>positive</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Basically there's a family where a little boy ...</td>\n",
       "      <td>negative</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Petter Mattei's \"Love in the Time of Money\" is...</td>\n",
       "      <td>positive</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49995</th>\n",
       "      <td>I thought this movie did a down right good job...</td>\n",
       "      <td>positive</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49996</th>\n",
       "      <td>Bad plot, bad dialogue, bad acting, idiotic di...</td>\n",
       "      <td>negative</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49997</th>\n",
       "      <td>I am a Catholic taught in parochial elementary...</td>\n",
       "      <td>negative</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49998</th>\n",
       "      <td>I'm going to have to disagree with the previou...</td>\n",
       "      <td>negative</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49999</th>\n",
       "      <td>No one expects the Star Trek movies to be high...</td>\n",
       "      <td>negative</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>50000 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                  review sentiment\n",
       "0      One of the other reviewers has mentioned that ...  positive\n",
       "1      A wonderful little production. <br /><br />The...  positive\n",
       "2      I thought this was a wonderful way to spend ti...  positive\n",
       "3      Basically there's a family where a little boy ...  negative\n",
       "4      Petter Mattei's \"Love in the Time of Money\" is...  positive\n",
       "...                                                  ...       ...\n",
       "49995  I thought this movie did a down right good job...  positive\n",
       "49996  Bad plot, bad dialogue, bad acting, idiotic di...  negative\n",
       "49997  I am a Catholic taught in parochial elementary...  negative\n",
       "49998  I'm going to have to disagree with the previou...  negative\n",
       "49999  No one expects the Star Trek movies to be high...  negative\n",
       "\n",
       "[50000 rows x 2 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv(\"assets\\IMDB Dataset.csv\")\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "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>review</th>\n",
       "      <th>sentiment</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>One of the other reviewers has mentioned that ...</td>\n",
       "      <td>positive</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>A wonderful little production. &lt;br /&gt;&lt;br /&gt;The...</td>\n",
       "      <td>positive</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>I thought this was a wonderful way to spend ti...</td>\n",
       "      <td>positive</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Basically there's a family where a little boy ...</td>\n",
       "      <td>negative</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Petter Mattei's \"Love in the Time of Money\" is...</td>\n",
       "      <td>positive</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49995</th>\n",
       "      <td>I thought this movie did a down right good job...</td>\n",
       "      <td>positive</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49996</th>\n",
       "      <td>Bad plot, bad dialogue, bad acting, idiotic di...</td>\n",
       "      <td>negative</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49997</th>\n",
       "      <td>I am a Catholic taught in parochial elementary...</td>\n",
       "      <td>negative</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49998</th>\n",
       "      <td>I'm going to have to disagree with the previou...</td>\n",
       "      <td>negative</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49999</th>\n",
       "      <td>No one expects the Star Trek movies to be high...</td>\n",
       "      <td>negative</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>50000 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                  review sentiment\n",
       "0      One of the other reviewers has mentioned that ...  positive\n",
       "1      A wonderful little production. <br /><br />The...  positive\n",
       "2      I thought this was a wonderful way to spend ti...  positive\n",
       "3      Basically there's a family where a little boy ...  negative\n",
       "4      Petter Mattei's \"Love in the Time of Money\" is...  positive\n",
       "...                                                  ...       ...\n",
       "49995  I thought this movie did a down right good job...  positive\n",
       "49996  Bad plot, bad dialogue, bad acting, idiotic di...  negative\n",
       "49997  I am a Catholic taught in parochial elementary...  negative\n",
       "49998  I'm going to have to disagree with the previou...  negative\n",
       "49999  No one expects the Star Trek movies to be high...  negative\n",
       "\n",
       "[50000 rows x 2 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\n",
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       "    }\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>sentiment</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>one of the other reviewers has mentioned that ...</td>\n",
       "      <td>positive</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>a wonderful little production. &lt;br /&gt;&lt;br /&gt;the...</td>\n",
       "      <td>positive</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>i thought this was a wonderful way to spend ti...</td>\n",
       "      <td>positive</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>basically there's a family where a little boy ...</td>\n",
       "      <td>negative</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>petter mattei's \"love in the time of money\" is...</td>\n",
       "      <td>positive</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49995</th>\n",
       "      <td>i thought this movie did a down right good job...</td>\n",
       "      <td>positive</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49996</th>\n",
       "      <td>bad plot, bad dialogue, bad acting, idiotic di...</td>\n",
       "      <td>negative</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49997</th>\n",
       "      <td>i am a catholic taught in parochial elementary...</td>\n",
       "      <td>negative</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49998</th>\n",
       "      <td>i'm going to have to disagree with the previou...</td>\n",
       "      <td>negative</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49999</th>\n",
       "      <td>no one expects the star trek movies to be high...</td>\n",
       "      <td>negative</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>50000 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                  review sentiment\n",
       "0      one of the other reviewers has mentioned that ...  positive\n",
       "1      a wonderful little production. <br /><br />the...  positive\n",
       "2      i thought this was a wonderful way to spend ti...  positive\n",
       "3      basically there's a family where a little boy ...  negative\n",
       "4      petter mattei's \"love in the time of money\" is...  positive\n",
       "...                                                  ...       ...\n",
       "49995  i thought this movie did a down right good job...  positive\n",
       "49996  bad plot, bad dialogue, bad acting, idiotic di...  negative\n",
       "49997  i am a catholic taught in parochial elementary...  negative\n",
       "49998  i'm going to have to disagree with the previou...  negative\n",
       "49999  no one expects the star trek movies to be high...  negative\n",
       "\n",
       "[50000 rows x 2 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['review'] = df['review'].str.lower()\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.drop_duplicates(inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "import re\n",
    "def rem_tags(text):\n",
    "    p= re.compile('<.*?>')\n",
    "    return p.sub(r'',text)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['review'] = df['review'].apply(rem_tags)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "from nltk.corpus import stopwords\n",
    "st_words= set(stopwords.words('english'))\n",
    "def rem_stopwords(text):\n",
    "    words= text.split()\n",
    "    fil_words= [word for word in words if word.lower() not in st_words]\n",
    "    return \" \".join(fil_words)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['review']=df['review'].apply(rem_stopwords)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "import string \n",
    "trash= string.punctuation\n",
    "\n",
    "def rem_punc(text):\n",
    "    tt= str.maketrans('','',trash)\n",
    "    return text.translate(tt)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['review'] = df['review'].apply(rem_punc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "from nltk.tokenize import word_tokenize\n",
    "from nltk.stem import PorterStemmer\n",
    "\n",
    "stemmer= PorterStemmer()\n",
    "\n",
    "df['tokenized_review']= df['review'].apply(lambda x: word_tokenize(x))\n",
    "df['tokenized_review'] = df['tokenized_review'].apply(lambda tokens: [stemmer.stem(token) for token in tokens])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "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>review</th>\n",
       "      <th>sentiment</th>\n",
       "      <th>tokenized_review</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>one reviewers mentioned watching 1 oz episode ...</td>\n",
       "      <td>positive</td>\n",
       "      <td>[one, review, mention, watch, 1, oz, episod, h...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>wonderful little production filming technique ...</td>\n",
       "      <td>positive</td>\n",
       "      <td>[wonder, littl, product, film, techniqu, unass...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>thought wonderful way spend time hot summer we...</td>\n",
       "      <td>positive</td>\n",
       "      <td>[thought, wonder, way, spend, time, hot, summe...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>basically theres family little boy jake thinks...</td>\n",
       "      <td>negative</td>\n",
       "      <td>[basic, there, famili, littl, boy, jake, think...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>petter matteis love time money visually stunni...</td>\n",
       "      <td>positive</td>\n",
       "      <td>[petter, mattei, love, time, money, visual, st...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                              review sentiment  \\\n",
       "0  one reviewers mentioned watching 1 oz episode ...  positive   \n",
       "1  wonderful little production filming technique ...  positive   \n",
       "2  thought wonderful way spend time hot summer we...  positive   \n",
       "3  basically theres family little boy jake thinks...  negative   \n",
       "4  petter matteis love time money visually stunni...  positive   \n",
       "\n",
       "                                    tokenized_review  \n",
       "0  [one, review, mention, watch, 1, oz, episod, h...  \n",
       "1  [wonder, littl, product, film, techniqu, unass...  \n",
       "2  [thought, wonder, way, spend, time, hot, summe...  \n",
       "3  [basic, there, famili, littl, boy, jake, think...  \n",
       "4  [petter, mattei, love, time, money, visual, st...  "
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>review</th>\n",
       "      <th>sentiment</th>\n",
       "      <th>tokenized_review</th>\n",
       "      <th>joined_review</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>one reviewers mentioned watching 1 oz episode ...</td>\n",
       "      <td>positive</td>\n",
       "      <td>[one, review, mention, watch, 1, oz, episod, h...</td>\n",
       "      <td>one review mention watch 1 oz episod hook righ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>wonderful little production filming technique ...</td>\n",
       "      <td>positive</td>\n",
       "      <td>[wonder, littl, product, film, techniqu, unass...</td>\n",
       "      <td>wonder littl product film techniqu unassum old...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>thought wonderful way spend time hot summer we...</td>\n",
       "      <td>positive</td>\n",
       "      <td>[thought, wonder, way, spend, time, hot, summe...</td>\n",
       "      <td>thought wonder way spend time hot summer weeke...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>basically theres family little boy jake thinks...</td>\n",
       "      <td>negative</td>\n",
       "      <td>[basic, there, famili, littl, boy, jake, think...</td>\n",
       "      <td>basic there famili littl boy jake think there ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>petter matteis love time money visually stunni...</td>\n",
       "      <td>positive</td>\n",
       "      <td>[petter, mattei, love, time, money, visual, st...</td>\n",
       "      <td>petter mattei love time money visual stun film...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49995</th>\n",
       "      <td>thought movie right good job creative original...</td>\n",
       "      <td>positive</td>\n",
       "      <td>[thought, movi, right, good, job, creativ, ori...</td>\n",
       "      <td>thought movi right good job creativ origin fir...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49996</th>\n",
       "      <td>bad plot bad dialogue bad acting idiotic direc...</td>\n",
       "      <td>negative</td>\n",
       "      <td>[bad, plot, bad, dialogu, bad, act, idiot, dir...</td>\n",
       "      <td>bad plot bad dialogu bad act idiot direct anno...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49997</th>\n",
       "      <td>catholic taught parochial elementary schools n...</td>\n",
       "      <td>negative</td>\n",
       "      <td>[cathol, taught, parochi, elementari, school, ...</td>\n",
       "      <td>cathol taught parochi elementari school nun ta...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49998</th>\n",
       "      <td>im going disagree previous comment side maltin...</td>\n",
       "      <td>negative</td>\n",
       "      <td>[im, go, disagre, previou, comment, side, malt...</td>\n",
       "      <td>im go disagre previou comment side maltin one ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49999</th>\n",
       "      <td>one expects star trek movies high art fans exp...</td>\n",
       "      <td>negative</td>\n",
       "      <td>[one, expect, star, trek, movi, high, art, fan...</td>\n",
       "      <td>one expect star trek movi high art fan expect ...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>49582 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                  review sentiment  \\\n",
       "0      one reviewers mentioned watching 1 oz episode ...  positive   \n",
       "1      wonderful little production filming technique ...  positive   \n",
       "2      thought wonderful way spend time hot summer we...  positive   \n",
       "3      basically theres family little boy jake thinks...  negative   \n",
       "4      petter matteis love time money visually stunni...  positive   \n",
       "...                                                  ...       ...   \n",
       "49995  thought movie right good job creative original...  positive   \n",
       "49996  bad plot bad dialogue bad acting idiotic direc...  negative   \n",
       "49997  catholic taught parochial elementary schools n...  negative   \n",
       "49998  im going disagree previous comment side maltin...  negative   \n",
       "49999  one expects star trek movies high art fans exp...  negative   \n",
       "\n",
       "                                        tokenized_review  \\\n",
       "0      [one, review, mention, watch, 1, oz, episod, h...   \n",
       "1      [wonder, littl, product, film, techniqu, unass...   \n",
       "2      [thought, wonder, way, spend, time, hot, summe...   \n",
       "3      [basic, there, famili, littl, boy, jake, think...   \n",
       "4      [petter, mattei, love, time, money, visual, st...   \n",
       "...                                                  ...   \n",
       "49995  [thought, movi, right, good, job, creativ, ori...   \n",
       "49996  [bad, plot, bad, dialogu, bad, act, idiot, dir...   \n",
       "49997  [cathol, taught, parochi, elementari, school, ...   \n",
       "49998  [im, go, disagre, previou, comment, side, malt...   \n",
       "49999  [one, expect, star, trek, movi, high, art, fan...   \n",
       "\n",
       "                                           joined_review  \n",
       "0      one review mention watch 1 oz episod hook righ...  \n",
       "1      wonder littl product film techniqu unassum old...  \n",
       "2      thought wonder way spend time hot summer weeke...  \n",
       "3      basic there famili littl boy jake think there ...  \n",
       "4      petter mattei love time money visual stun film...  \n",
       "...                                                  ...  \n",
       "49995  thought movi right good job creativ origin fir...  \n",
       "49996  bad plot bad dialogu bad act idiot direct anno...  \n",
       "49997  cathol taught parochi elementari school nun ta...  \n",
       "49998  im go disagre previou comment side maltin one ...  \n",
       "49999  one expect star trek movi high art fan expect ...  \n",
       "\n",
       "[49582 rows x 4 columns]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['joined_review'] =  df['tokenized_review'].apply(lambda x: ' '.join(x))\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "x_train,x_test,y_train,y_test = train_test_split(df['joined_review'],df['sentiment'],test_size=0.3,stratify=df['sentiment'],random_state=23)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.pipeline import Pipeline\n",
    "from sklearn.feature_extraction.text import CountVectorizer,TfidfTransformer\n",
    "from sklearn.naive_bayes import MultinomialNB\n",
    "from sklearn.metrics import classification_report"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "pipeline = Pipeline([('vect', CountVectorizer()),\n",
    "                    ('tfidf', TfidfTransformer()),\n",
    "                    ('nbclf', MultinomialNB())\n",
    "                    ])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-1 {color: black;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>Pipeline(steps=[(&#x27;vect&#x27;, CountVectorizer()), (&#x27;tfidf&#x27;, TfidfTransformer()),\n",
       "                (&#x27;nbclf&#x27;, MultinomialNB())])</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" ><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">Pipeline</label><div class=\"sk-toggleable__content\"><pre>Pipeline(steps=[(&#x27;vect&#x27;, CountVectorizer()), (&#x27;tfidf&#x27;, TfidfTransformer()),\n",
       "                (&#x27;nbclf&#x27;, MultinomialNB())])</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" ><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">CountVectorizer</label><div class=\"sk-toggleable__content\"><pre>CountVectorizer()</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-3\" type=\"checkbox\" ><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">TfidfTransformer</label><div class=\"sk-toggleable__content\"><pre>TfidfTransformer()</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-4\" type=\"checkbox\" ><label for=\"sk-estimator-id-4\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">MultinomialNB</label><div class=\"sk-toggleable__content\"><pre>MultinomialNB()</pre></div></div></div></div></div></div></div>"
      ],
      "text/plain": [
       "Pipeline(steps=[('vect', CountVectorizer()), ('tfidf', TfidfTransformer()),\n",
       "                ('nbclf', MultinomialNB())])"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pipeline.fit(x_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred= pipeline.predict(x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "    negative       0.86      0.87      0.86      7410\n",
      "    positive       0.87      0.85      0.86      7465\n",
      "\n",
      "    accuracy                           0.86     14875\n",
      "   macro avg       0.86      0.86      0.86     14875\n",
      "weighted avg       0.86      0.86      0.86     14875\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['sentiment_analysis.pkl']"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "joblib.dump(pipeline, 'sentiment_analysis.pkl')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "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>comment</th>\n",
       "      <th>likes</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Consistent carriapa is my fav genre</td>\n",
       "      <td>198</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>NN EP IS AN ABSOLUTE VIBE ️</td>\n",
       "      <td>253</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Hell yeahh a full fledged video on emiway ep</td>\n",
       "      <td>192</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>11:43 KSHAMA EP</td>\n",
       "      <td>62</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Bkp  don't hate anyone about his view/critics ...</td>\n",
       "      <td>105</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>307</th>\n",
       "      <td>Emiway \\nAs a singer \\nas a rapper</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>308</th>\n",
       "      <td>Umm i don't know but i feel emiways voice soun...</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>309</th>\n",
       "      <td>Beef ke baad ye sab kyu banjate Arijit</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>310</th>\n",
       "      <td>Femiway chhake ke gaane kon kon sunta h like k...</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>311</th>\n",
       "      <td>Beef ke baad ye saare kyun banjate Arijit.</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>312 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                               comment  likes\n",
       "0                  Consistent carriapa is my fav genre    198\n",
       "1                          NN EP IS AN ABSOLUTE VIBE ️    253\n",
       "2        Hell yeahh a full fledged video on emiway ep     192\n",
       "3                                      11:43 KSHAMA EP     62\n",
       "4    Bkp  don't hate anyone about his view/critics ...    105\n",
       "..                                                 ...    ...\n",
       "307                 Emiway \\nAs a singer \\nas a rapper      0\n",
       "308  Umm i don't know but i feel emiways voice soun...      0\n",
       "309             Beef ke baad ye sab kyu banjate Arijit      0\n",
       "310  Femiway chhake ke gaane kon kon sunta h like k...      0\n",
       "311         Beef ke baad ye saare kyun banjate Arijit.      0\n",
       "\n",
       "[312 rows x 2 columns]"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "yt = pd.read_csv(\"assets\\youtube_comments2.csv\")\n",
    "yt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "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>comment</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Consistent carriapa is my fav genre</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>NN EP IS AN ABSOLUTE VIBE ️</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Hell yeahh a full fledged video on emiway ep</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>11:43 KSHAMA EP</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Bkp  don't hate anyone about his view/critics ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>307</th>\n",
       "      <td>Emiway \\nAs a singer \\nas a rapper</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>308</th>\n",
       "      <td>Umm i don't know but i feel emiways voice soun...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>309</th>\n",
       "      <td>Beef ke baad ye sab kyu banjate Arijit</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>310</th>\n",
       "      <td>Femiway chhake ke gaane kon kon sunta h like k...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>311</th>\n",
       "      <td>Beef ke baad ye saare kyun banjate Arijit.</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>312 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                               comment\n",
       "0                  Consistent carriapa is my fav genre\n",
       "1                          NN EP IS AN ABSOLUTE VIBE ️\n",
       "2        Hell yeahh a full fledged video on emiway ep \n",
       "3                                      11:43 KSHAMA EP\n",
       "4    Bkp  don't hate anyone about his view/critics ...\n",
       "..                                                 ...\n",
       "307                 Emiway \\nAs a singer \\nas a rapper\n",
       "308  Umm i don't know but i feel emiways voice soun...\n",
       "309             Beef ke baad ye sab kyu banjate Arijit\n",
       "310  Femiway chhake ke gaane kon kon sunta h like k...\n",
       "311         Beef ke baad ye saare kyun banjate Arijit.\n",
       "\n",
       "[312 rows x 1 columns]"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "yt.drop(columns=['likes'],inplace=True)\n",
    "yt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 312 entries, 0 to 311\n",
      "Data columns (total 1 columns):\n",
      " #   Column   Non-Null Count  Dtype \n",
      "---  ------   --------------  ----- \n",
      " 0   comment  310 non-null    object\n",
      "dtypes: object(1)\n",
      "memory usage: 2.6+ KB\n"
     ]
    }
   ],
   "source": [
    "yt.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "yt['comment'] = yt['comment'].fillna('')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "y_pred= pipeline.predict(yt['comment'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['positive' 'positive' 'positive' 'positive' 'positive' 'negative'\n",
      " 'negative' 'positive' 'positive' 'positive' 'positive' 'negative'\n",
      " 'positive' 'positive' 'positive' 'negative' 'positive' 'positive'\n",
      " 'positive' 'negative' 'positive' 'positive' 'negative' 'positive'\n",
      " 'negative' 'positive' 'positive' 'positive' 'positive' 'positive'\n",
      " 'positive' 'negative' 'negative' 'positive' 'positive' 'positive'\n",
      " 'positive' 'positive' 'negative' 'negative' 'positive' 'positive'\n",
      " 'positive' 'positive' 'negative' 'positive' 'negative' 'negative'\n",
      " 'positive' 'negative' 'positive' 'positive' 'positive' 'positive'\n",
      " 'positive' 'negative' 'positive' 'positive' 'negative' 'negative'\n",
      " 'negative' 'positive' 'positive' 'positive' 'negative' 'positive'\n",
      " 'negative' 'positive' 'positive' 'negative' 'positive' 'negative'\n",
      " 'positive' 'negative' 'positive' 'positive' 'positive' 'positive'\n",
      " 'positive' 'positive' 'positive' 'negative' 'positive' 'negative'\n",
      " 'positive' 'negative' 'positive' 'negative' 'positive' 'negative'\n",
      " 'negative' 'negative' 'positive' 'negative' 'positive' 'negative'\n",
      " 'positive' 'positive' 'positive' 'positive' 'negative' 'negative'\n",
      " 'positive' 'negative' 'positive' 'positive' 'positive' 'positive'\n",
      " 'positive' 'negative' 'positive' 'positive' 'positive' 'positive'\n",
      " 'positive' 'negative' 'positive' 'negative' 'positive' 'negative'\n",
      " 'positive' 'negative' 'positive' 'negative' 'negative' 'negative'\n",
      " 'negative' 'positive' 'positive' 'positive' 'positive' 'negative'\n",
      " 'negative' 'positive' 'positive' 'negative' 'negative' 'positive'\n",
      " 'negative' 'negative' 'negative' 'negative' 'positive' 'positive'\n",
      " 'positive' 'positive' 'positive' 'positive' 'positive' 'positive'\n",
      " 'negative' 'negative' 'positive' 'negative' 'positive' 'negative'\n",
      " 'positive' 'positive' 'positive' 'positive' 'positive' 'positive'\n",
      " 'negative' 'negative' 'positive' 'positive' 'positive' 'negative'\n",
      " 'negative' 'negative' 'negative' 'negative' 'positive' 'positive'\n",
      " 'positive' 'positive' 'negative' 'positive' 'positive' 'positive'\n",
      " 'positive' 'positive' 'positive' 'positive' 'negative' 'negative'\n",
      " 'positive' 'negative' 'negative' 'negative' 'positive' 'negative'\n",
      " 'negative' 'negative' 'positive' 'positive' 'positive' 'positive'\n",
      " 'negative' 'positive' 'positive' 'positive' 'positive' 'negative'\n",
      " 'positive' 'negative' 'negative' 'positive' 'positive' 'positive'\n",
      " 'positive' 'negative' 'positive' 'positive' 'positive' 'negative'\n",
      " 'negative' 'negative' 'positive' 'positive' 'negative' 'negative'\n",
      " 'negative' 'positive' 'positive' 'negative' 'negative' 'positive'\n",
      " 'negative' 'negative' 'positive' 'positive' 'positive' 'positive'\n",
      " 'positive' 'negative' 'positive' 'positive' 'positive' 'positive'\n",
      " 'negative' 'negative' 'positive' 'positive' 'positive' 'negative'\n",
      " 'negative' 'positive' 'positive' 'positive' 'positive' 'positive'\n",
      " 'positive' 'negative' 'positive' 'positive' 'positive' 'positive'\n",
      " 'positive' 'negative' 'positive' 'positive' 'negative' 'negative'\n",
      " 'positive' 'positive' 'positive' 'positive' 'positive' 'negative'\n",
      " 'positive' 'positive' 'negative' 'positive' 'negative' 'positive'\n",
      " 'positive' 'positive' 'negative' 'positive' 'positive' 'negative'\n",
      " 'positive' 'negative' 'positive' 'positive' 'negative' 'positive'\n",
      " 'positive' 'positive' 'negative' 'positive' 'positive' 'negative'\n",
      " 'negative' 'negative' 'positive' 'positive' 'negative' 'positive'\n",
      " 'negative' 'negative' 'positive' 'positive' 'positive' 'positive'\n",
      " 'positive' 'negative' 'negative' 'positive' 'positive' 'positive']\n"
     ]
    }
   ],
   "source": [
    "print(y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "comments = []\n",
    "predictions = []\n",
    "\n",
    "\n",
    "for i in yt['comment']:\n",
    "    y_pred = pipeline.predict([i])  \n",
    "    comments.append(i)\n",
    "    predictions.append(y_pred[0])  \n",
    "\n",
    "\n",
    "result_df = pd.DataFrame({\n",
    "    'comment': comments,\n",
    "    'predicted_sentiment': predictions\n",
    "})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "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>comment</th>\n",
       "      <th>predicted_sentiment</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Consistent carriapa is my fav genre</td>\n",
       "      <td>positive</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>NN EP IS AN ABSOLUTE VIBE ️</td>\n",
       "      <td>positive</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Hell yeahh a full fledged video on emiway ep</td>\n",
       "      <td>positive</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>11:43 KSHAMA EP</td>\n",
       "      <td>positive</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Bkp  don't hate anyone about his view/critics ...</td>\n",
       "      <td>positive</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>307</th>\n",
       "      <td>Emiway \\nAs a singer \\nas a rapper</td>\n",
       "      <td>negative</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>308</th>\n",
       "      <td>Umm i don't know but i feel emiways voice soun...</td>\n",
       "      <td>negative</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>309</th>\n",
       "      <td>Beef ke baad ye sab kyu banjate Arijit</td>\n",
       "      <td>positive</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>310</th>\n",
       "      <td>Femiway chhake ke gaane kon kon sunta h like k...</td>\n",
       "      <td>positive</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>311</th>\n",
       "      <td>Beef ke baad ye saare kyun banjate Arijit.</td>\n",
       "      <td>positive</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>312 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                               comment predicted_sentiment\n",
       "0                  Consistent carriapa is my fav genre            positive\n",
       "1                          NN EP IS AN ABSOLUTE VIBE ️            positive\n",
       "2        Hell yeahh a full fledged video on emiway ep             positive\n",
       "3                                      11:43 KSHAMA EP            positive\n",
       "4    Bkp  don't hate anyone about his view/critics ...            positive\n",
       "..                                                 ...                 ...\n",
       "307                 Emiway \\nAs a singer \\nas a rapper            negative\n",
       "308  Umm i don't know but i feel emiways voice soun...            negative\n",
       "309             Beef ke baad ye sab kyu banjate Arijit            positive\n",
       "310  Femiway chhake ke gaane kon kon sunta h like k...            positive\n",
       "311         Beef ke baad ye saare kyun banjate Arijit.            positive\n",
       "\n",
       "[312 rows x 2 columns]"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result_df"
   ]
  }
 ],
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