Tweet Sentiment Analysis System

A full-stack application to analyze tweet sentiment using FastAPI, Streamlit, Docker, and CI/CD automation.

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Features

• Real-time sentiment analysis powered by VADER.
• Interactive web interface built with Streamlit.
• Robust REST API designed with FastAPI.
• Containerized services managed via Docker Compose.
• Automated CI/CD pipeline using GitHub Actions.
• Comprehensive visualizations of sentiment data.
• Full test coverage for quality assurance.

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Quick Start

Using Docker Compose (Recommended)

1. Clone the repository: git clone https://github.com/sam20704/tweet-sentiment-analyzer.git cd tweet-sentiment-analyzer

2. Build and start the containers: docker-compose up --build

3. Access the application in your browser:

• Frontend UI: http://localhost:8501
• Backend API: http://localhost:8000
• API Docs (Swagger UI): http://localhost:8000/docs

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Local Development

1. Set up the Python environment and install dependencies: make install

2. Run the backend server: make run-backend

3. In a new terminal, run the frontend app: make run-frontend

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API Endpoints

Method	Endpoint	Description
GET	/	Basic health check
GET	/health	Detailed health status
POST	/analyze	Analyze tweet sentiment

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Testing

Run all tests with the following command:

make test

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Architecture Overview

┌───────────────┐ ┌───────────────┐ ┌───────────────┐ │ Frontend │ ──▶ │ Backend │ ──▶ │ VADER │ │ (Streamlit) │ │ (FastAPI) │ │ Sentiment │ │ Port: 8501 │ │ Port: 8000 │ │ Analyzer │ └───────────────┘ └───────────────┘ └───────────────┘

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Contributing

1. Fork the repository.
2. Create a feature branch (git checkout -b feature/my-feature).
3. Implement changes and add tests.
4. Run tests with make test.
5. Submit a pull request for review.

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License

This project is licensed under the MIT License. Built with FastAPI, Streamlit & Docker | Powered by VADER Sentiment Analysis