feat : Added WhatsApp Chat Analysis Project

Whatsapp Chats Sentiment Analysis/README.md

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+# WhatsApp Chats Sentiment Analysis
+
+## Overview
+This project performs sentiment analysis on WhatsApp chat data using Natural Language Processing (NLP) techniques. The implementation extracts chat messages from a WhatsApp export file, processes the text data, and analyzes sentiment patterns using VADER (Valence Aware Dictionary and sentiment Reasoner).
+
+## Features
+- WhatsApp chat file parsing and message extraction
+- Multiline message handling
+- Sentiment analysis using NLTK's VADER
+- Visualization of sentiment distribution (Positive, Negative, Neutral)
+
+
+## Technologies Used
+- Python 3
+- Pandas & NumPy for data manipulation
+- NLTK for sentiment analysis
+- Matplotlib & Seaborn for visualization
+- Emoji library for special character handling
+- Regular expressions for text parsing
+
+## Installation
+1. Clone this repository
+2. Install required packages:
+
+
+## Usage
+1. Export your WhatsApp chat as a .txt file (without media)
+2. Place the file in the project directory
+3. Update the file path in the notebook:
+```python
+conversation = r"your_whatsapp_chat.txt"
+```
+4. Run the Jupyter notebook cells sequentially
+
+## Code Structure
+1. **Data Extraction**: Parses WhatsApp chat format and extracts messages with metadata
+2. **Data Cleaning**: Handles multiline messages and missing values
+3. **Sentiment Analysis**: Uses VADER to compute positive, negative, and neutral scores
+4. **Visualization**: Generates bar charts showing sentiment distribution
+
+## Results
+The analysis provides:
+- Average sentiment scores for the conversation
+- Visual representation of sentiment distribution
+- Message-level sentiment scoring
+
+## Note
+- The implementation handles WhatsApp's specific date-time format and message structure
+- Supports both 12-hour time formats
+- Properly processes messages with emojis and special characters
+- Maintains message context across line breaks
+
+## Privacy
+This tool processes chat data locally. No data is sent to external servers, ensuring your conversations remain private.
+

Whatsapp Chats Sentiment Analysis/Whatsapp Chats Sentiment Analysis.ipynb

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+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "571ba0f0-de49-4f1d-b0c5-2627d9575bc1",
+   "metadata": {},
+   "source": [
+    "**Implementation**"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "4a14af81-8abc-41d1-8253-a0d4cebf8025",
+   "metadata": {},
+   "source": [
+    "Importing the required libraries"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "91a3c082-7476-4b12-baf5-6172999ef0af",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "!pip install emoji\n",
+    "!pip install wordcloud\n",
+    "import re\n",
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "import emoji\n",
+    "from collections import Counter\n",
+    "import matplotlib.pyplot as plt\n",
+    "from PIL import Image\n",
+    "from wordcloud import WordCloud, STOPWORDS, ImageColorGenerator\n",
+    "import nltk\n",
+    "from nltk.sentiment.vader import SentimentIntensityAnalyzer\n",
+    "import seaborn as sns\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "cfd7181c-20b3-4605-89cc-67a05d3e1bd5",
+   "metadata": {},
+   "source": [
+    "Define the File Path & Open and Read the File"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "c3b74922-04f6-4526-a9bf-049d8b5a3845",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "conversation = r\"whatspp group chat txt file.txt\"\n",
+    "\n",
+    "with open(conversation, \"r\", encoding=\"utf-8\") as file:\n",
+    "    lines = file.readlines()\n",
+    "\n",
+    "print(f\"Total lines in chat file: {len(lines)}\")\n",
+    "print(\"\\nFirst 10 lines from the file:\")\n",
+    "for i in range(min(10, len(lines))):\n",
+    "    print(lines[i].strip())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5de5da7c-b79c-40a0-b7db-18f361398e47",
+   "metadata": {},
+   "source": [
+    "Identification of whether a line from a WhatsApp chat file starts with a timestamp."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "28df0be9-e25c-479a-9785-a60a05411833",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import re\n",
+    "\n",
+    "def date_time(s):\n",
+    "    pattern = r'^(\\d{1,2})/(\\d{1,2})/(\\d{2,4}), (\\d{1,2}):(\\d{2}) ?(AM|PM|am|pm)? -'\n",
+    "    return bool(re.match(pattern, s))\n",
+    "\n",
+    "# Test on first 10 lines from the chat file\n",
+    "for line in lines[:10]:\n",
+    "    print(f\"{line.strip()} → {date_time(line)}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "9bf78892-9c78-4855-8c7a-7f077109439b",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def getMessage(line):\n",
+    "    if \" - \" not in line:\n",
+    "        return None, None, None, None  # Skip invalid lines\n",
+    "\n",
+    "    splitline = line.split(\" - \", 1)\n",
+    "    datetime_part = splitline[0]\n",
+    "    \n",
+    "    try:\n",
+    "        date, time = datetime_part.split(\", \", 1)\n",
+    "    except ValueError:\n",
+    "        return None, None, None, None  # Skip invalid lines\n",
+    "    \n",
+    "    message_part = splitline[1]\n",
+    "    if \": \" in message_part:\n",
+    "        author, message = message_part.split(\": \", 1)\n",
+    "    else:\n",
+    "        author, message = None, message_part  # No contact name found\n",
+    "    \n",
+    "    return date, time, author, message"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "a12a35bf-1127-4c14-8598-668cc6e462d8",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "for line in lines[:10]:  \n",
+    "    print(getMessage(line))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3cd59572-ffe0-46db-98fb-7636f66a7f44",
+   "metadata": {},
+   "source": [
+    "Extracts structured message data from a WhatsApp chat file and stores it in a list. It correctly handles multiline messages, ensuring they are grouped with their respective timestamps and authors."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "42224c23-b168-4c6e-9576-657eb09f2b62",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data = []\n",
+    "messageBuffer = []\n",
+    "date, time, author = None, None, None\n",
+    "\n",
+    "for line in lines:\n",
+    "    line = line.strip()\n",
+    "    if not line:\n",
+    "        continue  # Skip empty lines\n",
+    "\n",
+    "    if date_time(line):  # If it's a new message\n",
+    "        if messageBuffer:\n",
+    "            data.append([date, time, author, ' '.join(messageBuffer)])\n",
+    "        messageBuffer.clear()\n",
+    "        date, time, author, message = getMessage(line)\n",
+    "        messageBuffer.append(message)\n",
+    "    else:\n",
+    "        messageBuffer.append(line)  # Append multiline messages\n",
+    "\n",
+    "if messageBuffer:\n",
+    "    data.append([date, time, author, ' '.join(messageBuffer)])\n",
+    "\n",
+    "print(f\"Total messages extracted: {len(data)}\")\n",
+    "print(data[:5])  # Show first 5 extracted messages\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "9d029402-54c2-477f-bda8-976a543413a3",
+   "metadata": {},
+   "source": [
+    "Sentiment of WhatsApp chat messages using NLTK's VADER Sentiment Analysis."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "684780ea-a33c-4905-b5ca-769fa7bc427b",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#Convert Extracted Data into a Pandas DataFrame\n",
+    "df = pd.DataFrame(data, columns=[\"Date\", \"Time\", \"Contact\", \"Message\"])\n",
+    "\n",
+    "#Ensure Data is Clean\n",
+    "if df.empty:\n",
+    "    print(\"No messages extracted. Fix chat parsing first.\")\n",
+    "else:\n",
+    "    df['Date'] = pd.to_datetime(df['Date'])\n",
+    "    df.dropna(inplace=True)\n",
+    "\n",
+    "\n",
+    "# Initialize Sentiment Analyzer\n",
+    "sentiments = SentimentIntensityAnalyzer()\n",
+    "\n",
+    "# Apply Sentiment Analysis\n",
+    "df[\"Positive\"] = df[\"Message\"].astype(str).apply(lambda x: sentiments.polarity_scores(x)[\"pos\"])\n",
+    "df[\"Negative\"] = df[\"Message\"].astype(str).apply(lambda x: sentiments.polarity_scores(x)[\"neg\"])\n",
+    "df[\"Neutral\"] = df[\"Message\"].astype(str).apply(lambda x: sentiments.polarity_scores(x)[\"neu\"])\n",
+    "\n",
+    "# Display first 5 messages\n",
+    "pd.set_option('display.width', 200)  # Adjust width for better formatting\n",
+    "print(df.head(25).to_string(index=False))\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "619059af-5f11-4d21-a122-dc13ef1381bc",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Sentiment Visualization (Positive, Neutral, Negative Messages)\n",
+    "plt.figure(figsize=(10, 5))\n",
+    "sentiment_counts = df[[\"Positive\", \"Negative\", \"Neutral\"]].mean()\n",
+    "sentiment_counts.plot(kind=\"bar\", color=[\"green\", \"red\", \"blue\"])\n",
+    "plt.title(\"Sentiment Analysis of Chat Messages\")\n",
+    "plt.ylabel(\"Average Sentiment Score\")\n",
+    "plt.xticks(rotation=0)\n",
+    "plt.show()"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python [conda env:base] *",
+   "language": "python",
+   "name": "conda-base-py"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.12.3"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}