4.4 Practical Task.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 4.5 Practical Task"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import re\n",
    "from vaderSentiment.vaderSentiment import SentimentIntensityAnalyzer\n",
    "import transformers\n",
    "from transformers import pipeline\n",
    "from nltk.tokenize import word_tokenize\n",
    "from nltk.corpus import stopwords\n",
    "from nltk.stem import WordNetLemmatizer\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn import metrics\n",
    "from itertools import chain\n",
    "from nltk import NaiveBayesClassifier"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Load data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "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>index</th>\n",
       "      <th>reviewText</th>\n",
       "      <th>rating</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>11494</td>\n",
       "      <td>Clean and funny. A bit busy with all the diffe...</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>984</td>\n",
       "      <td>Alex a sexy hot cop and the PhD candidate. Wha...</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1463</td>\n",
       "      <td>Good thing that this is a free story. I read i...</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>10342</td>\n",
       "      <td>Action, action, action! Equipment keeps gettin...</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5256</td>\n",
       "      <td>this was hands down the worse book i have ever...</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   index                                         reviewText  rating\n",
       "0  11494  Clean and funny. A bit busy with all the diffe...       3\n",
       "1    984  Alex a sexy hot cop and the PhD candidate. Wha...       4\n",
       "2   1463  Good thing that this is a free story. I read i...       1\n",
       "3  10342  Action, action, action! Equipment keeps gettin...       4\n",
       "4   5256  this was hands down the worse book i have ever...       1"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = pd.read_csv(\"book_reviews_sample.csv\")\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 100 entries, 0 to 99\n",
      "Data columns (total 3 columns):\n",
      " #   Column      Non-Null Count  Dtype \n",
      "---  ------      --------------  ----- \n",
      " 0   index       100 non-null    int64 \n",
      " 1   reviewText  100 non-null    object\n",
      " 2   rating      100 non-null    int64 \n",
      "dtypes: int64(2), object(1)\n",
      "memory usage: 2.5+ KB\n"
     ]
    }
   ],
   "source": [
    "data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Clean and funny. A bit busy with all the different plots going on. But overall a good read. Bye now.'"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['reviewText'][0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Clean data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# lowercase\n",
    "data['reviewText_clean'] = data['reviewText'].str.lower()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# remove punctuation\n",
    "data['reviewText_clean'] = data.apply(lambda x: re.sub(r\"([^\\w\\s])\", \"\", x['reviewText_clean']), axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "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>index</th>\n",
       "      <th>reviewText</th>\n",
       "      <th>rating</th>\n",
       "      <th>reviewText_clean</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>11494</td>\n",
       "      <td>Clean and funny. A bit busy with all the diffe...</td>\n",
       "      <td>3</td>\n",
       "      <td>clean and funny a bit busy with all the differ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>984</td>\n",
       "      <td>Alex a sexy hot cop and the PhD candidate. Wha...</td>\n",
       "      <td>4</td>\n",
       "      <td>alex a sexy hot cop and the phd candidate what...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1463</td>\n",
       "      <td>Good thing that this is a free story. I read i...</td>\n",
       "      <td>1</td>\n",
       "      <td>good thing that this is a free story i read it...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>10342</td>\n",
       "      <td>Action, action, action! Equipment keeps gettin...</td>\n",
       "      <td>4</td>\n",
       "      <td>action action action equipment keeps getting b...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5256</td>\n",
       "      <td>this was hands down the worse book i have ever...</td>\n",
       "      <td>1</td>\n",
       "      <td>this was hands down the worse book i have ever...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   index                                         reviewText  rating  \\\n",
       "0  11494  Clean and funny. A bit busy with all the diffe...       3   \n",
       "1    984  Alex a sexy hot cop and the PhD candidate. Wha...       4   \n",
       "2   1463  Good thing that this is a free story. I read i...       1   \n",
       "3  10342  Action, action, action! Equipment keeps gettin...       4   \n",
       "4   5256  this was hands down the worse book i have ever...       1   \n",
       "\n",
       "                                    reviewText_clean  \n",
       "0  clean and funny a bit busy with all the differ...  \n",
       "1  alex a sexy hot cop and the phd candidate what...  \n",
       "2  good thing that this is a free story i read it...  \n",
       "3  action action action equipment keeps getting b...  \n",
       "4  this was hands down the worse book i have ever...  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Rule-based sentiment with VADER"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "vader_sentiment = SentimentIntensityAnalyzer()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "data['vader_sentiment_score'] = data['reviewText_clean'].apply(lambda review: vader_sentiment.polarity_scores(review)['compound'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "# create labels\n",
    "bins = [-1, -0.1, 0.1, 1]\n",
    "names = ['negative', 'neutral', 'positive']\n",
    "\n",
    "data['vader_sentiment_label'] = pd.cut(data['vader_sentiment_score'], bins, labels=names)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "data['vader_sentiment_label'].value_counts().plot.bar()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Pre-trained Transformer Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "transformer_pipeline = pipeline(\"sentiment-analysis\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "transformer_labels = []\n",
    "\n",
    "for review in data['reviewText_clean'].values:\n",
    "    sentiment_list = transformer_pipeline(review)\n",
    "    sentiment_label = [sent['label'] for sent in sentiment_list]\n",
    "    transformer_labels.append(sentiment_label)\n",
    "    \n",
    "data['transformer_sentiment_label'] = transformer_labels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "data['transformer_sentiment_label'].value_counts().plot.bar()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}