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WhatsApp Chat Analyzer

Analyze your WhatsApp conversations to uncover insights about communication patterns, emoji usage, and participant activity! 🚀

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📋 Table of Contents

• About the Project
• Features
• Technologies Used
• Getting Started
• Usage
• Future Enhancements
• Contributing
• License

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📖 About the Project

The WhatsApp Chat Analyzer enables users to extract meaningful insights from their WhatsApp chat data. Whether you want to discover the most active participant, analyze emoji usage, or observe overall communication trends, this tool makes it simple and interactive!

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✨ Features

• Participant Activity Analysis: Identify the most active participants based on message counts.
• Emoji Insights: Discover the most commonly used emojis in your chats.
• Communication Patterns: Observe trends such as the balance of contributions between participants.
• Interactive Visualizations: Graphs and charts to make your data come alive.

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🛠 Technologies Used

• Python: Core programming language for data analysis and processing.
• Pandas: For data manipulation and analysis.
• Matplotlib: For generating visual insights like bar charts and pie charts.
• Emoji Library: For extracting and analyzing emoji data.
• Streamlit: For building the interactive user interface.

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🚀 Getting Started

Prerequisites

1. Install Python 3.8 or above.
2. Install required libraries:

   pip install pandas matplotlib emoji streamlit

Setup

1. Clone the repository:

   git clone https://github.com/your-username/whatsapp-chat-analyzer.git

2. Navigate to the project directory:

   cd whatsapp-chat-analyzer

3. Run the Streamlit app:

   streamlit run app.py

4. Upload your WhatsApp chat export (in .txt format) to start the analysis.

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💡 Usage

1. Export your WhatsApp chat in .txt format:
   • Go to your WhatsApp chat > Options > Export Chat > Without Media.
2. Upload the .txt file into the application.
3. View insights such as:
   • Top Contributors: See who sends the most messages.
   • Emoji Usage: Analyze emotional expressions.
   • Trends and Patterns: Observe engagement levels over time.

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🔮 Future Enhancements

• Sentiment Analysis: Understand the emotional tone of conversations.
• Advanced Visualizations: Interactive network graphs and word clouds.
• NLP Integration: Topic extraction and text classification for deeper insights.

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🤝 Contributing

We welcome contributions! If you have ideas to improve this project, feel free to:

1. Fork the repository.
2. Create a new branch for your feature:

   git checkout -b feature-name

3. Commit your changes:

   git commit -m "Added new feature"

4. Push to your branch and open a Pull Request.

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📜 License

This project is licensed under the MIT License. See the LICENSE file for details.

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📧 Contact

For questions or suggestions, feel free to reach out:

• Email: sonjevilas2002@gmail.com
• GitHub: SonjeVilas

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Give this project a ⭐ if you found it helpful!

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