README.md

WhatsApp Chat Analyzer

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

Table of Contents

1. System Overview
2. Architecture
3. Components
4. Data Flow
5. Installation
6. Usage
7. Analysis Capabilities

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:

   git clone [repository-url]
   cd whatsapp-analyzer
   

2. Install dependencies:

   pip install -r requirements.txt
   

3. Run the application:

   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