sentiment_analyse.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "cd31b8d4",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Table 'SentimentAnalyse' created\n",
      "negative reviews: 14 (saved in db)\n",
      "neutral reviews: 27 (saved in db)\n",
      "positive reviews: 188 (saved in db)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import csv\n",
    "import re\n",
    "import json\n",
    "import matplotlib.pyplot as plt\n",
    "from database import (drop_table,conn,cursor,create_table)\n",
    "\n",
    "table_name = \"SentimentAnalyse\"\n",
    "drop_table(table_name)\n",
    "create_table(table_name, \"type\",\"total\")\n",
    "\n",
    "\n",
    "def insert_data(type, total ):\n",
    "    cursor.execute(f\"INSERT INTO {table_name} (type, total) VALUES (?, ?)\", type, total)\n",
    "    print(f\"{type} reviews: {total} (saved in db)\")\n",
    "    conn.commit()\n",
    "    \n",
    "# Load word list from json file\n",
    "def get_word_list(category):\n",
    "    with open(\"data/\"+category + '.json', 'r') as f:\n",
    "        word_list = json.load(f)\n",
    "    return word_list\n",
    "\n",
    "# Determine sentiment of a review\n",
    "def get_sentiment(review):\n",
    "    review = re.sub('[^\\w\\s]', '', review).lower()\n",
    "    positive_words = get_word_list('positive_words')\n",
    "    negative_words = get_word_list('negative_words')\n",
    "    words = review.split()\n",
    "    positive_count = 0\n",
    "    negative_count = 0\n",
    "    for word in words:\n",
    "        if word[:-1] in [w.lower() for w in positive_words] or word in [w.lower() for w in positive_words]:\n",
    "            positive_count += 1 \n",
    "        elif word in [w.lower() for w in negative_words] or word[:-1] in [w.lower() for w in negative_words]:\n",
    "            negative_count += 1\n",
    "    if positive_count > negative_count:\n",
    "        return 'positive'\n",
    "    elif negative_count > positive_count:\n",
    "        return 'negative'\n",
    "    else:\n",
    "        return 'neutral'\n",
    "\n",
    "# Initialize sentiment counter\n",
    "reviews_sentiment = {\"positive\": 0, \"negative\": 0, \"neutral\": 0 }\n",
    "\n",
    "# Read reviews from CSV file and update sentiment counter\n",
    "with open('data/reviews.csv', newline='') as csvfile:\n",
    "    reader = csv.DictReader(csvfile)\n",
    "    for row in reader:\n",
    "        if row['title']:    \n",
    "            sentiment = get_sentiment(row['title'])\n",
    "            reviews_sentiment[sentiment] += 1\n",
    "\n",
    "# Print sentiment counts\n",
    "for sentiment in ['negative', 'neutral', 'positive']:\n",
    "    insert_data(sentiment, reviews_sentiment[sentiment])\n",
    "\n",
    "# Plot sentiment bar chart\n",
    "left = [1, 2, 3]\n",
    "tick_label = ['Negative', 'Neutral', 'Positive']\n",
    "plt.bar(left, height=(reviews_sentiment['negative'],reviews_sentiment['neutral'],reviews_sentiment['positive']), \n",
    "        tick_label=tick_label, width=0.8, color=( \"#FA332D\",\"#3E3F3F\",\"#06B076\"))\n",
    "plt.title('Customer Satisfaction Statistics')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "65fefe76",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c4ac6152",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.10.9"
  },
  "vscode": {
   "interpreter": {
    "hash": "3c06e3e46abf38078fe4dac36a0085ec2b134ebbd73dd076183d243eeca6918f"
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}