README.md

# WhatsApp Chat Analyzer

A comprehensive tool for analyzing WhatsApp chat exports with sentiment analysis capabilities.

## Table of Contents
1. [System Overview](#system-overview)
2. [Architecture](#architecture)
3. [Components](#components)
4. [Data Flow](#data-flow)
5. [Installation](#installation)
6. [Usage](#usage)
7. [Analysis Capabilities](#analysis-capabilities)
8. 

## System Overview

The WhatsApp Chat Analyzer is a Python-based application that processes exported WhatsApp chat data to provide:
- Message statistics and metrics
- Temporal activity patterns
- User engagement analysis
- Content analysis (words, emojis, links)
- Sentiment analysis capabilities
- Topics analysis in the group chats

Built with Streamlit for the web interface, it offers an interactive way to explore chat dynamics and analyze sentiment.

## Architecture

The system follows a modular architecture with clear separation of concerns:

```
Raw WhatsApp Chat → Preprocessing → Analysis → Visualization
```

Key architectural decisions:
- **Modular Design**: Components are separated by functionality
- **Pipeline Processing**: Data flows through discrete processing stages
- **Interactive UI**: Streamlit enables real-time exploration

## Components

### 1. App Module (`app.py`)
- **Responsibility**: User interface and visualization
- **Key Features**:
  - File upload handling
  - User selection interface
  - Visualization rendering
  - Interactive controls

### 2. Preprocessor (`preprocessor.py`)
- **Responsibility**: Data cleaning and structuring
- **Key Features**:
  - Handles multiple date/time formats
  - Extracts messages and metadata
  - Filters system messages
  - Creates structured DataFrame

### 3. Helper Module (`helper.py`)
- **Responsibility**: Analytical computations
- **Key Features**:
  - Statistical metrics
  - Temporal analysis
  - Content analysis
  - Visualization data preparation

### 4. Notebook (`whatsAppAnalyzer.ipynb`)
- **Responsibility**: Prototyping and experimentation
- **Key Features**:
  - Initial pattern development
  - Data exploration
  - Algorithm testing

## Data Flow

1. **Input**: User uploads WhatsApp chat export (.txt)
2. **Preprocessing**:
   - Raw text is parsed using regex patterns
   - Messages are categorized and timestamped
   - Structured DataFrame is created
3. **Analysis**:
   - Selected metrics are computed
   - Temporal patterns are identified
   - Content features are extracted
4. **Visualization**:
   - Results are displayed in interactive charts
   - User can explore different views

## Installation

### Prerequisites
- Python 3.8+
- pip package manager

### Steps
1. Clone the repository:
   ```bash
   git clone [repository-url]
   cd whatsapp-analyzer
   ```

2. Install dependencies:
   ```bash
   pip install -r requirements.txt
   ```

3. Run the application:
   ```bash
   streamlit run srcs/app.py
   ```

## Usage

1. Launch the application
2. Upload a WhatsApp chat export file
3. Select a user or "Overall" for group analysis
4. Explore the various analysis tabs:
   - Statistics
   - Timelines
   - Activity Maps
   - Word Clouds
   - Emoji Analysis

## Analysis Capabilities

### 1. Basic Statistics
- Message counts
- Word counts
- Media shared
- Links shared

### 2. Temporal Analysis
- Daily activity patterns
- Monthly trends
- Hourly distributions

### 3. User Engagement
- Most active users
- User participation rates
- Message distribution

### 4. Content Analysis
- Most common words
- Emoji usage

### 5. Sentiment Analysis
- Message sentiment scoring
- Sentiment trends over time
- User sentiment comparison
## 5. Topics Analysis
- Topic modeling
- Common topics over time
- User interests