Sentiment Analyzer

A full-stack sentiment analysis application with separate frontend and backend services. Built with Next.js frontend and FastAPI backend.

🚀 Features

• Real-time Sentiment Analysis: Analyze text sentiment using Naive Bayes classifier
• Modern Web Interface: Built with Next.js 14 and Tailwind CSS
• RESTful API: FastAPI backend with automatic documentation
• Machine Learning: Trained on movie reviews dataset using NLTK and scikit-learn
• Independent Services: Frontend and backend can be deployed separately

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🏗️ Architecture

• Frontend: Next.js 14 (React/TypeScript)
• Backend: FastAPI (Python)
• ML Library: scikit-learn with NLTK
• Styling: Tailwind CSS with shadcn/ui components

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📁 Project Structure

sentiment-analyzer/
├── frontend/               # Next.js frontend
│   ├── app/               # Next.js app directory
│   ├── components/        # React components
│   ├── lib/              # Utility functions
│   ├── package.json      # Node.js dependencies
│   └── next.config.mjs   # Next.js configuration
├── backend/               # FastAPI backend
│   ├── main.py           # FastAPI application
│   ├── requirements.txt  # Python dependencies
│   └── README.md         # Backend documentation
├── venv/                 # Python virtual environment
└── README.md             # This file

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🛠️ Local Development

Prerequisites

• Python 3.11+
• Node.js 18+
• npm or yarn

Backend Setup

1. Create and activate virtual environment:

   python -m venv venv
   
   # Windows
   .\venv\Scripts\Activate.ps1
   
   # Linux/Mac
   source venv/bin/activate

2. Install Python dependencies:

   pip install -r backend/requirements.txt

3. Run the backend:

   python backend/main.py

   The API will be available at http://localhost:8000

Frontend Setup

1. Install Node.js dependencies:

   cd frontend
   npm install

2. Run the development server:

   npm run dev

   The frontend will be available at http://localhost:3000

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📚 API Documentation

Once the backend is running, visit:

• API Docs: http://localhost:8000/docs
• ReDoc: http://localhost:8000/redoc

API Endpoints

• GET / - API information
• GET /api/health - Health check endpoint
• POST /api/predict - Analyze sentiment
• GET /api/demo - Demo with sample predictions

Example API Usage

# Analyze sentiment
curl -X POST "http://localhost:8000/api/predict" \
     -H "Content-Type: application/json" \
     -d '{"text": "I love this movie!"}'

# Response
{
  "text": "I love this movie!",
  "sentiment": "pos",
  "confidence": 0.85
}

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🧪 Testing

Backend Testing

# Health check
curl http://localhost:8000/api/health

# Sentiment analysis
curl -X POST "http://localhost:8000/api/predict" \
     -H "Content-Type: application/json" \
     -d '{"text": "This is amazing!"}'

Frontend Testing

Visit http://localhost:3000 and test the web interface.

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🔧 Development

Backend Development

• Framework: FastAPI
• ML Libraries: scikit-learn, NLTK
• Model: Naive Bayes classifier trained on movie reviews
• Features: Text preprocessing, feature extraction, sentiment prediction

Frontend Development

• Framework: Next.js 14 with App Router
• Styling: Tailwind CSS
• Components: shadcn/ui component library
• TypeScript: Full type safety

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📦 Dependencies

Backend (Python)

• FastAPI 0.104.1
• Uvicorn 0.24.0
• NLTK 3.9.1
• scikit-learn 1.5.2
• NumPy 2.3.3
• SciPy 1.16.2

Frontend (Node.js)

• Next.js 14.2.16
• React 18
• TypeScript 5
• Tailwind CSS 3.4.0
• shadcn/ui components

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🤝 Contributing

1. Fork the repository
2. Create a feature branch
3. Make your changes
4. Test thoroughly
5. Submit a pull request

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🚀 Live Demo

Frontend: Sentiment Analyzer Link

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🙏 Acknowledgments

• Movie reviews dataset from NLTK
• FastAPI for the excellent web framework
• Next.js team for the React framework
• shadcn/ui for the beautiful components