4.4 Practical Task.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 4.4 Practical Task"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "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\") # the data should be in the same folder as your notebook\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": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# lowercase\n",
    "data['reviewText_clean'] = data['reviewText'].str.lower()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "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": 7,
   "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": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Rule-based sentiment with VADER"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "vader_sentiment = SentimentIntensityAnalyzer()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "data['vader_sentiment_score'] = data['reviewText_clean'].apply(lambda review: vader_sentiment.polarity_scores(review)['compound'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "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": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='vader_sentiment_label'>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "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": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "No model was supplied, defaulted to distilbert/distilbert-base-uncased-finetuned-sst-2-english and revision 714eb0f (https://huggingface.co/distilbert/distilbert-base-uncased-finetuned-sst-2-english).\n",
      "Using a pipeline without specifying a model name and revision in production is not recommended.\n",
      "Device set to use cpu\n"
     ]
    }
   ],
   "source": [
    "transformer_pipeline = pipeline(\"sentiment-analysis\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "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": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='transformer_sentiment_label'>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data['transformer_sentiment_label'].value_counts().plot.bar()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "nlp_course_env",
   "language": "python",
   "name": "nlp_course_env"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.11"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}