https://github.com/tusharpandey003/chat_analysis

Analysis of group chat with respect to individual member of group
https://github.com/tusharpandey003/chat_analysis

chat-analysis chat-analyzer data-analysis data-science streamlit whatsapp whatsapp-chat whatsapp-web

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Analysis of group chat with respect to individual member of group

• Host: GitHub
• URL: https://github.com/tusharpandey003/chat_analysis
• Owner: tusharpandey003
• Created: 2024-05-12T15:01:34.000Z (6 months ago)
• Default Branch: main
• Last Pushed: 2024-05-17T14:54:22.000Z (6 months ago)
• Last Synced: 2024-09-30T07:40:56.921Z (about 2 months ago)
• Topics: chat-analysis, chat-analyzer, data-analysis, data-science, streamlit, whatsapp, whatsapp-chat, whatsapp-web
• Language: Jupyter Notebook
• Homepage:
• Size: 17.6 KB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
# Chat Analysis with Streamlit App

Overview

The WhatsApp Chat Analysis repository hosts a powerful and user-friendly Streamlit application designed to analyze and visualize WhatsApp group chat data.

Whether you’re curious about your group’s activity patterns, individual user contributions, or insights derived from timestamps, this app provides a comprehensive toolkit for extracting valuable information from your chat history.

Key Features

1. Monthly and Weekly Activity

The app displays an intuitive dashboard that breaks down chat activity by month and week. You can quickly identify peak usage periods, spot trends, and understand when your group is most active.

2. Group Chat Analysis

Dive deeper into group dynamics by examining overall message counts, media sharing, and popular keywords. The app generates word clouds and frequency charts to highlight recurring themes.

3. Individual User Message Analysis

Wondering who the chattiest member is? The app profiles each user’s contribution, including the number of messages sent, media shared, and average message length. It even identifies the most common emojis used by each participant.

4. User Activity Tracing

Track individual user activity over time. The app visualizes how often each member participates, allowing you to spot lurkers, active contributors, and occasional participants.

5. Insights from Date and Time Data

Timestamps hold valuable clues. The app extracts insights such as:

Peak Hours: Discover when your group is most active during the day.

Weekday vs. Weekend Trends: Understand how chat patterns vary between weekdays and weekends.

Late-Night Conversations: Identify those midnight discussions.

How It Works

Upload Your Chat Data: Simply upload your WhatsApp chat export (in text format) to the app.

Explore the Dashboard: Navigate through the different sections to explore insights.

### Installation:

Clone this repository to your local machine.

Install the required Python packages using:

pip install -r requirements.txt

### Run the Streamlit app using:

streamlit run app.py

Contribution

Feel free to contribute by adding new features, improving visualizations, or enhancing the app’s functionality. Pull requests are welcome!

License

This project is licensed under the MIT License. You’re free to use, modify, and distribute it as needed.

Whether you’re a data enthusiast, a curious group admin, or just someone who loves digging into chat histories, the WhatsApp Chat Analysis with Streamlit App will empower you with valuable insights. 

Happy analyzing!