This is a full-stack data science application built with Python and Streamlit that analyzes real-time data from the Twitter/X API for any given topic. It provides an AI-generated summary, sentiment analysis, key theme extraction, and named entity recognition.
- AI-Powered Summaries: Uses the Google Gemini API to generate dynamic summaries of the conversation.
- Topic-Based Analysis: Employs a relational PostgreSQL database to store and analyze tweets on a per-topic basis, ensuring data integrity.
- Detailed NLP Insights: Performs sentiment analysis, key theme extraction (TF-IDF), and Named Entity Recognition (spaCy).
- Interactive Dashboard: Built with Streamlit for a user-friendly experience.
- Clone the repository:
git clone <repository-url> - Navigate into the project directory:
cd <repository-name> - Create a virtual environment:
python -m venv venv - Activate it:
venv\Scripts\activate.bat - Install required packages:
pip install -r requirements.txt - Set up your API keys and database URL in
.streamlit/secrets.toml. - Run the database setup script:
python setup_database.py - Run the Streamlit app:
streamlit run app.py