Amazon Reviews - Sentiment Analysis.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Amazon Reviews - Sentiment Analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 1. Sentimental Analysis using VADER ( Valence Aware dictionary and Sentiment Reasoner)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "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>label</th>\n",
       "      <th>review</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>pos</td>\n",
       "      <td>Stuning even for the non-gamer: This sound tra...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>pos</td>\n",
       "      <td>The best soundtrack ever to anything.: I'm rea...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>pos</td>\n",
       "      <td>Amazing!: This soundtrack is my favorite music...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>pos</td>\n",
       "      <td>Excellent Soundtrack: I truly like this soundt...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>pos</td>\n",
       "      <td>Remember, Pull Your Jaw Off The Floor After He...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "  label                                             review\n",
       "0   pos  Stuning even for the non-gamer: This sound tra...\n",
       "1   pos  The best soundtrack ever to anything.: I'm rea...\n",
       "2   pos  Amazing!: This soundtrack is my favorite music...\n",
       "3   pos  Excellent Soundtrack: I truly like this soundt...\n",
       "4   pos  Remember, Pull Your Jaw Off The Floor After He..."
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "# import the reviews dataset\n",
    "df = pd.read_csv(r'C:\\Users\\sanya\\Desktop\\sanya\\sentiment analysis\\amazonreviews.tsv', sep='\\t')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "neg    5097\n",
       "pos    4903\n",
       "Name: label, dtype: int64"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# to check the labels\n",
    "df['label'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "label     0\n",
       "review    0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# remove nan values and empty strings \n",
    "df.dropna(inplace = True)\n",
    "blanks = []\n",
    "for i, lb, rv in df.itertuples():\n",
    "    if type(rv)==str:\n",
    "        if rv.isspace():\n",
    "            blanks.append(i)\n",
    "df.drop(blanks, inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "neg    5097\n",
       "pos    4903\n",
       "Name: label, dtype: int64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# check the labels again, they are just like before.This means there were no nan values or empty strings\n",
    "df['label'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# import sentiment Inensity Analyzer to run on the tsv file\n",
    "from nltk.sentiment.vader import SentimentIntensityAnalyzer\n",
    "sid = SentimentIntensityAnalyzer()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'neg': 0.088, 'neu': 0.669, 'pos': 0.243, 'compound': 0.9454}"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# this is the polarity score of the first entry in the dataset, we need to this for all entries in the dataset\n",
    "sid.polarity_scores(df.loc[0]['review'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'pos'"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# this is the label of the first entry in the dataset, we need to this for all entries in the dataset \n",
    "#and then compare the predicted sentiment to check the accuracy\n",
    "df.loc[0]['label']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>label</th>\n",
       "      <th>review</th>\n",
       "      <th>scores</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>pos</td>\n",
       "      <td>Stuning even for the non-gamer: This sound tra...</td>\n",
       "      <td>{'neg': 0.088, 'neu': 0.669, 'pos': 0.243, 'co...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>pos</td>\n",
       "      <td>The best soundtrack ever to anything.: I'm rea...</td>\n",
       "      <td>{'neg': 0.018, 'neu': 0.837, 'pos': 0.145, 'co...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>pos</td>\n",
       "      <td>Amazing!: This soundtrack is my favorite music...</td>\n",
       "      <td>{'neg': 0.04, 'neu': 0.692, 'pos': 0.268, 'com...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>pos</td>\n",
       "      <td>Excellent Soundtrack: I truly like this soundt...</td>\n",
       "      <td>{'neg': 0.09, 'neu': 0.615, 'pos': 0.295, 'com...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>pos</td>\n",
       "      <td>Remember, Pull Your Jaw Off The Floor After He...</td>\n",
       "      <td>{'neg': 0.0, 'neu': 0.746, 'pos': 0.254, 'comp...</td>\n",
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      "text/plain": [
       "  label                                             review  \\\n",
       "0   pos  Stuning even for the non-gamer: This sound tra...   \n",
       "1   pos  The best soundtrack ever to anything.: I'm rea...   \n",
       "2   pos  Amazing!: This soundtrack is my favorite music...   \n",
       "3   pos  Excellent Soundtrack: I truly like this soundt...   \n",
       "4   pos  Remember, Pull Your Jaw Off The Floor After He...   \n",
       "\n",
       "                                              scores  \n",
       "0  {'neg': 0.088, 'neu': 0.669, 'pos': 0.243, 'co...  \n",
       "1  {'neg': 0.018, 'neu': 0.837, 'pos': 0.145, 'co...  \n",
       "2  {'neg': 0.04, 'neu': 0.692, 'pos': 0.268, 'com...  \n",
       "3  {'neg': 0.09, 'neu': 0.615, 'pos': 0.295, 'com...  \n",
       "4  {'neg': 0.0, 'neu': 0.746, 'pos': 0.254, 'comp...  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# calculating the polarity score of all the entries in the dataset\n",
    "df['scores'] = df['review'].apply(lambda review: sid.polarity_scores(review))\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>label</th>\n",
       "      <th>review</th>\n",
       "      <th>scores</th>\n",
       "      <th>compound</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>pos</td>\n",
       "      <td>Stuning even for the non-gamer: This sound tra...</td>\n",
       "      <td>{'neg': 0.088, 'neu': 0.669, 'pos': 0.243, 'co...</td>\n",
       "      <td>0.9454</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>pos</td>\n",
       "      <td>The best soundtrack ever to anything.: I'm rea...</td>\n",
       "      <td>{'neg': 0.018, 'neu': 0.837, 'pos': 0.145, 'co...</td>\n",
       "      <td>0.8957</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>pos</td>\n",
       "      <td>Amazing!: This soundtrack is my favorite music...</td>\n",
       "      <td>{'neg': 0.04, 'neu': 0.692, 'pos': 0.268, 'com...</td>\n",
       "      <td>0.9858</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>pos</td>\n",
       "      <td>Excellent Soundtrack: I truly like this soundt...</td>\n",
       "      <td>{'neg': 0.09, 'neu': 0.615, 'pos': 0.295, 'com...</td>\n",
       "      <td>0.9814</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>pos</td>\n",
       "      <td>Remember, Pull Your Jaw Off The Floor After He...</td>\n",
       "      <td>{'neg': 0.0, 'neu': 0.746, 'pos': 0.254, 'comp...</td>\n",
       "      <td>0.9781</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "  label                                             review  \\\n",
       "0   pos  Stuning even for the non-gamer: This sound tra...   \n",
       "1   pos  The best soundtrack ever to anything.: I'm rea...   \n",
       "2   pos  Amazing!: This soundtrack is my favorite music...   \n",
       "3   pos  Excellent Soundtrack: I truly like this soundt...   \n",
       "4   pos  Remember, Pull Your Jaw Off The Floor After He...   \n",
       "\n",
       "                                              scores  compound  \n",
       "0  {'neg': 0.088, 'neu': 0.669, 'pos': 0.243, 'co...    0.9454  \n",
       "1  {'neg': 0.018, 'neu': 0.837, 'pos': 0.145, 'co...    0.8957  \n",
       "2  {'neg': 0.04, 'neu': 0.692, 'pos': 0.268, 'com...    0.9858  \n",
       "3  {'neg': 0.09, 'neu': 0.615, 'pos': 0.295, 'com...    0.9814  \n",
       "4  {'neg': 0.0, 'neu': 0.746, 'pos': 0.254, 'comp...    0.9781  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# calculating the compounded score to ultimately label the statement with the sentiment the model thinks applies on it\n",
    "df['compound'] = df['scores'].apply(lambda score_dict:score_dict['compound'])\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
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       "      <th>label</th>\n",
       "      <th>review</th>\n",
       "      <th>scores</th>\n",
       "      <th>compound</th>\n",
       "      <th>comp_score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>pos</td>\n",
       "      <td>Stuning even for the non-gamer: This sound tra...</td>\n",
       "      <td>{'neg': 0.088, 'neu': 0.669, 'pos': 0.243, 'co...</td>\n",
       "      <td>0.9454</td>\n",
       "      <td>pos</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>pos</td>\n",
       "      <td>The best soundtrack ever to anything.: I'm rea...</td>\n",
       "      <td>{'neg': 0.018, 'neu': 0.837, 'pos': 0.145, 'co...</td>\n",
       "      <td>0.8957</td>\n",
       "      <td>pos</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>pos</td>\n",
       "      <td>Amazing!: This soundtrack is my favorite music...</td>\n",
       "      <td>{'neg': 0.04, 'neu': 0.692, 'pos': 0.268, 'com...</td>\n",
       "      <td>0.9858</td>\n",
       "      <td>pos</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>pos</td>\n",
       "      <td>Excellent Soundtrack: I truly like this soundt...</td>\n",
       "      <td>{'neg': 0.09, 'neu': 0.615, 'pos': 0.295, 'com...</td>\n",
       "      <td>0.9814</td>\n",
       "      <td>pos</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>pos</td>\n",
       "      <td>Remember, Pull Your Jaw Off The Floor After He...</td>\n",
       "      <td>{'neg': 0.0, 'neu': 0.746, 'pos': 0.254, 'comp...</td>\n",
       "      <td>0.9781</td>\n",
       "      <td>pos</td>\n",
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      "text/plain": [
       "  label                                             review  \\\n",
       "0   pos  Stuning even for the non-gamer: This sound tra...   \n",
       "1   pos  The best soundtrack ever to anything.: I'm rea...   \n",
       "2   pos  Amazing!: This soundtrack is my favorite music...   \n",
       "3   pos  Excellent Soundtrack: I truly like this soundt...   \n",
       "4   pos  Remember, Pull Your Jaw Off The Floor After He...   \n",
       "\n",
       "                                              scores  compound comp_score  \n",
       "0  {'neg': 0.088, 'neu': 0.669, 'pos': 0.243, 'co...    0.9454        pos  \n",
       "1  {'neg': 0.018, 'neu': 0.837, 'pos': 0.145, 'co...    0.8957        pos  \n",
       "2  {'neg': 0.04, 'neu': 0.692, 'pos': 0.268, 'com...    0.9858        pos  \n",
       "3  {'neg': 0.09, 'neu': 0.615, 'pos': 0.295, 'com...    0.9814        pos  \n",
       "4  {'neg': 0.0, 'neu': 0.746, 'pos': 0.254, 'comp...    0.9781        pos  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# model assigns the sentiment attached to the statement\n",
    "df['comp_score'] = df['compound'].apply(lambda c:'pos' if c>=0 else 'neg')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7091"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# In the table printed above, we need to compare the label column with the comp_score\n",
    "#to check the accuracy of the sentiments predicted\n",
    "from sklearn.metrics import accuracy_score\n",
    "accuracy_score(df['label'], df['comp_score'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 2. Sentimental Analysis using sklearn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>label</th>\n",
       "      <th>review</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>pos</td>\n",
       "      <td>Stuning even for the non-gamer: This sound tra...</td>\n",
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       "      <th>1</th>\n",
       "      <td>pos</td>\n",
       "      <td>The best soundtrack ever to anything.: I'm rea...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>pos</td>\n",
       "      <td>Amazing!: This soundtrack is my favorite music...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>pos</td>\n",
       "      <td>Excellent Soundtrack: I truly like this soundt...</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>pos</td>\n",
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      "text/plain": [
       "  label                                             review\n",
       "0   pos  Stuning even for the non-gamer: This sound tra...\n",
       "1   pos  The best soundtrack ever to anything.: I'm rea...\n",
       "2   pos  Amazing!: This soundtrack is my favorite music...\n",
       "3   pos  Excellent Soundtrack: I truly like this soundt...\n",
       "4   pos  Remember, Pull Your Jaw Off The Floor After He..."
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# load the reviews dataset\n",
    "ds = pd.read_csv(r'C:\\Users\\sanya\\Desktop\\sanya\\sentiment analysis\\amazonreviews.tsv', sep='\\t')\n",
    "ds.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "label     0\n",
       "review    0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ds.isnull().sum() #to check for blank statements and nan values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "# splitting data into training and testing\n",
    "from sklearn.model_selection import train_test_split\n",
    "x=ds['review']\n",
    "y=ds['label']\n",
    "x_train, x_test, y_train, y_test = train_test_split(x,y,test_size = 0.20, random_state = 5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.pipeline import Pipeline\n",
    "from sklearn.feature_extraction.text import TfidfVectorizer\n",
    "from sklearn.naive_bayes import MultinomialNB\n",
    "from sklearn.svm import LinearSVC\n",
    "\n",
    "text_clf_nb = Pipeline([('tfidf', TfidfVectorizer()), ('clf',MultinomialNB()),])\n",
    "text_clf_lsvc = Pipeline([('tfidf',TfidfVectorizer()),('clf',LinearSVC()),])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Pipeline(memory=None,\n",
       "         steps=[('tfidf',\n",
       "                 TfidfVectorizer(analyzer='word', binary=False,\n",
       "                                 decode_error='strict',\n",
       "                                 dtype=<class 'numpy.float64'>,\n",
       "                                 encoding='utf-8', input='content',\n",
       "                                 lowercase=True, max_df=1.0, max_features=None,\n",
       "                                 min_df=1, ngram_range=(1, 1), norm='l2',\n",
       "                                 preprocessor=None, smooth_idf=True,\n",
       "                                 stop_words=None, strip_accents=None,\n",
       "                                 sublinear_tf=False,\n",
       "                                 token_pattern='(?u)\\\\b\\\\w\\\\w+\\\\b',\n",
       "                                 tokenizer=None, use_idf=True,\n",
       "                                 vocabulary=None)),\n",
       "                ('clf',\n",
       "                 MultinomialNB(alpha=1.0, class_prior=None, fit_prior=True))],\n",
       "         verbose=False)"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "text_clf_nb.fit(x_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "predictions = text_clf_nb.predict(x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.8275\n"
     ]
    }
   ],
   "source": [
    "import sklearn.metrics as metrics\n",
    "print(metrics.accuracy_score(y_test,predictions))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Pipeline(memory=None,\n",
       "         steps=[('tfidf',\n",
       "                 TfidfVectorizer(analyzer='word', binary=False,\n",
       "                                 decode_error='strict',\n",
       "                                 dtype=<class 'numpy.float64'>,\n",
       "                                 encoding='utf-8', input='content',\n",
       "                                 lowercase=True, max_df=1.0, max_features=None,\n",
       "                                 min_df=1, ngram_range=(1, 1), norm='l2',\n",
       "                                 preprocessor=None, smooth_idf=True,\n",
       "                                 stop_words=None, strip_accents=None,\n",
       "                                 sublinear_tf=False,\n",
       "                                 token_pattern='(?u)\\\\b\\\\w\\\\w+\\\\b',\n",
       "                                 tokenizer=None, use_idf=True,\n",
       "                                 vocabulary=None)),\n",
       "                ('clf',\n",
       "                 LinearSVC(C=1.0, class_weight=None, dual=True,\n",
       "                           fit_intercept=True, intercept_scaling=1,\n",
       "                           loss='squared_hinge', max_iter=1000,\n",
       "                           multi_class='ovr', penalty='l2', random_state=None,\n",
       "                           tol=0.0001, verbose=0))],\n",
       "         verbose=False)"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "text_clf_lsvc.fit(x_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "predictions = text_clf_lsvc.predict(x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.879\n"
     ]
    }
   ],
   "source": [
    "print(metrics.accuracy_score(y_test, predictions))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Pipeline(memory=None,\n",
       "         steps=[('tfidf',\n",
       "                 TfidfVectorizer(analyzer='word', binary=False,\n",
       "                                 decode_error='strict',\n",
       "                                 dtype=<class 'numpy.float64'>,\n",
       "                                 encoding='utf-8', input='content',\n",
       "                                 lowercase=True, max_df=1.0, max_features=None,\n",
       "                                 min_df=1, ngram_range=(1, 1), norm='l2',\n",
       "                                 preprocessor=None, smooth_idf=True,\n",
       "                                 stop_words=frozenset({'a', 'about', 'above',\n",
       "                                                       'across', 'after',\n",
       "                                                       'afterward...\n",
       "                                                       'anything', 'anyway',\n",
       "                                                       'anywhere', ...}),\n",
       "                                 strip_accents=None, sublinear_tf=False,\n",
       "                                 token_pattern='(?u)\\\\b\\\\w\\\\w+\\\\b',\n",
       "                                 tokenizer=None, use_idf=True,\n",
       "                                 vocabulary=None)),\n",
       "                ('clf',\n",
       "                 LinearSVC(C=1.0, class_weight=None, dual=True,\n",
       "                           fit_intercept=True, intercept_scaling=1,\n",
       "                           loss='squared_hinge', max_iter=1000,\n",
       "                           multi_class='ovr', penalty='l2', random_state=None,\n",
       "                           tol=0.0001, verbose=0))],\n",
       "         verbose=False)"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.feature_extraction import text\n",
    "text_clf_lsvc2 = Pipeline([('tfidf',TfidfVectorizer(stop_words = text.ENGLISH_STOP_WORDS)),('clf',LinearSVC()),])\n",
    "text_clf_lsvc2.fit(x_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.879\n"
     ]
    }
   ],
   "source": [
    "print(metrics.accuracy_score(y_test,predictions))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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