WhatsApp Chat Analyzer

A Flask web application that analyzes WhatsApp chat exports using NLP techniques.

Features

• Upload and parse WhatsApp chat export files (.txt)
• User activity analysis (top contributors, message patterns)
• Sentiment analysis with VADER and TextBlob
• Topic modeling with LDA
• Emoji usage analysis
• Interactive dashboard with visualizations

Installation

1. Clone this repository
2. Create a virtual environment:

   python -m venv venv
   source venv/bin/activate  # On Windows: venv\Scripts\activate
   

3. Install dependencies:

   pip install -r requirements.txt
   

4. Download required NLTK data:

   python -c "import nltk; nltk.download('punkt'); nltk.download('stopwords'); nltk.download('wordnet')"
   

5. Download spaCy model:

   python -m spacy download en_core_web_sm
   

Usage

1. Run the development server:

   python run.py
   

2. Access the application at http://localhost:5000
3. Upload a WhatsApp chat export file and explore the analysis

Project Structure

WhatsApp-Chat-Analyzer/
├── app/                     # Main application package
│   ├── __init__.py          # Initialize Flask app
│   ├── config.py            # Configuration settings
│   ├── routes.py            # URL routes and views
│   ├── models/              # Data models
│   ├── services/            # Business logic and services
│   │   ├── parser.py        # Chat parsing module
│   │   ├── analyzer.py      # NLP analysis module 
│   │   ├── visualizer.py    # Data visualization module
│   │   └── nlp/             # NLP components
│   │       ├── sentiment.py # Sentiment analysis
│   │       ├── topics.py    # Topic modeling
│   │       └── emoji.py     # Emoji analysis
│   ├── static/              # Static files (CSS, JS, images)
│   └── templates/           # HTML templates
├── tests/                   # Unit and integration tests
├── data/                    # Sample data and outputs
├── logs/                    # Application logs
├── notebooks/               # Development notebooks
├── venv/                    # Virtual environment (gitignored)
├── run.py                   # Application entry point
├── requirements.txt         # Project dependencies
└── README.md                # Project documentation

License

MIT