Social Media Analysis

Hackonauts is a comprehensive social media analysis project built using Langflow and Python APIs, leveraging a pre-fed dataset. It includes features like sentiment analysis, trend identification, and user engagement metrics. Additionally, the project integrates a ChatBot powered by DataStax Astra and OpenAI API Key, offering interactive and user-friendly conversational insights.

🌐 Website

Access the live project at:
https://hackonauts.streamlit.app/

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👥 Team Members

• Vraj Patel: vraj2010
• Dhwani Vyas: dhwani1608
• Prince Thakarar: princethakarar
• Niva Amrutia: NivaA121

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📖 Features

• Data Analysis: Perform in-depth analysis on pre-fed social media data.
• Sentiment Analysis: Evaluate the sentiment of posts and comments.
• Trend Identification: Identify trending topics and hashtags.
• User Engagement Metrics: Analyze engagement levels such as likes, shares, and comments.
• Interactive Visualizations: Visualize data insights using charts and graphs.
• ChatBot Integration: Engage with a conversational chatbot powered by DataStax Astra and OpenAI for instant insights and assistance.

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🚀 Tech Stack

• Langflow: For streamlined workflow and AI-powered processing.
• Python: Core programming language for analysis and backend processing.
• Streamlit: For building an interactive and user-friendly web interface.
• DataStax Astra: Database solution for efficient and scalable data storage.
• OpenAI API: Advanced AI capabilities for building the ChatBot.

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📂 Dataset

The project uses a pre-fed dataset, formatted to include fields such as:

1. User_ID: Unique identifier for the user.
2. Followers_Count: Number of followers the user has.
3. Post_Type: Type of post (e.g., text, image, video, etc.).
4. Post_Length: Length of the post (measured in characters or words).
5. Post_Frequency: Frequency of posts made by the user.
6. Likes_Received: Total number of likes received on the user's posts.
7. Comments_Received: Total number of comments received on the user's posts.
8. Share_Count: Number of times the post was shared.
9. Engagement_Score: A calculated score indicating user engagement (e.g., based on likes, comments, and shares).
10. Popularity_Level: Categorization of popularity (e.g., high, medium, low).

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🖥 Screenshots

Langflow Setup

DataStax Astra Setup

ChatBot Interface

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📜 Installation and Setup

1. Clone the repository

   git clone https://github.com/your-repo/social-media-analysis.git
   cd social-media-analysis

2. Install dependencies

   pip install -r requirements.txt

3. Configure API Keys

   • Set up your OpenAI API Key in the environment variables:

     export OPENAI_API_KEY="your_openai_api_key"

   • Set up your DataStax Astra credentials:
     Follow the DataStax Astra setup guide to download and configure your credentials.

4. Run the Streamlit App

   streamlit run app.py

5. Access the app at http://localhost:8501.

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🎯 How to Use

1. Open the website.
2. Interact with the chatbot for insights or assistance.
3. Explore interactive visualizations and insights from the dataset.

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🤝 Contributing

We welcome contributions! To contribute:

1. Fork the repository.
2. Create a new branch for your feature or fix.
3. Submit a pull request with detailed descriptions.

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🛠️ Future Enhancements

• Add support for real-time social media data fetching.
• Expand to multiple languages for sentiment analysis.
• Enhance ChatBot functionality for more conversational abilities.

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📧 Contact

For any queries or suggestions, please contact:
Email: vraj20102005@gmail.com
GitHub: vraj2010

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💡 Acknowledgements

Special thanks to the Hackonauts team for their innovative contributions to this project!