SentimentSync - Full-Stack Sentiment Analysis Web App

A comprehensive social media sentiment analysis platform built with React, FastAPI, and machine learning.

🚀 Features

• CSV Upload & Data Preview: Drag-and-drop CSV upload with automatic sentiment analysis
• ML Model Training: Logistic Regression and Naive Bayes models with evaluation metrics
• Interactive Dashboard: Real-time charts and sentiment distribution analytics
• AI-Powered Insights: Claude API integration for intelligent analysis
• Real-Time Social Search: Dynamic social media content generation with sentiment analysis
• Trending Topics Discovery: Real-time trending topics from multiple platforms
• Advanced Search: Multi-platform search with filtering options
• Topic Insights: Comprehensive analysis with AI-powered summaries

🛠️ Tech Stack

Frontend

• React 18 with Vite
• TailwindCSS for styling
• Lucide React for icons
• Axios for API calls
• Chart.js for data visualization

Backend

• FastAPI for REST API
• Python 3.12 with async support
• NLTK for NLP preprocessing
• scikit-learn for ML models
• TextBlob for sentiment analysis
• Pandas for data processing

Deployment

• GitHub for version control
• Render for hosting
• Docker for containerization

📋 Prerequisites

• Python 3.12+
• Node.js 18+
• Git
• GitHub account
• Render account

🚀 Quick Start

Local Development

1. Clone the repository

git clone https://github.com/murtuja55/SentimentSync.git
cd SentimentSync

2. Backend Setup

cd backend
python -m venv venv
source venv/bin/activate  # On Windows: venv\Scripts\activate
pip install -r requirements.txt
python -m uvicorn main:app --reload --port 8000

3. Frontend Setup

cd frontend
npm install
npm run dev

4. Access the application

• Frontend: http://localhost:5173
• Backend API: http://localhost:8000
• API Docs: http://localhost:8000/docs

🌐 Deployment

GitHub Deployment

1. Push to GitHub

git add .
git commit -m "Initial commit"
git branch -M main
git remote add origin https://github.com/murtuja55/SentimentSync.git
git push -u origin main

2. Repository Structure

SentimentSync/
├── backend/
│   ├── main.py
│   ├── requirements.txt
│   ├── .env.example
│   └── ...
├── frontend/
│   ├── src/
│   ├── package.json
│   ├── vite.config.js
│   └── ...
├── README.md
├── .gitignore
├── render.yaml
└── docker-compose.yml

Render Deployment

Backend Deployment

1. Create Render Web Service

   • Go to Render Dashboard
   • Click "New" → "Web Service"
   • Connect your GitHub repository
   • Select "Python" as the runtime
   • Build command: pip install -r requirements.txt
   • Start command: uvicorn main:app --host 0.0.0.0 --port $PORT

2. Environment Variables

   PYTHON_VERSION=3.12
   FASTAPI_ENV=production
   CLAUDE_API_KEY=your_claude_api_key
   

Frontend Deployment

1. Create Static Site

   • Click "New" → "Static Site"
   • Connect your GitHub repository
   • Build command: cd frontend && npm run build
   • Publish directory: frontend/dist

2. Environment Variables

   NODE_VERSION=18
   VITE_API_URL=https://your-backend-url.onrender.com
   

🔧 Configuration

Backend Configuration

Create .env file in backend directory:

# Claude API
CLAUDE_API_KEY=your_claude_api_key_here

# Social Media APIs (Optional)
TWITTER_API_KEY=your_twitter_api_key
REDDIT_CLIENT_ID=your_reddit_client_id
REDDIT_CLIENT_SECRET=your_reddit_client_secret

# Production
FASTAPI_ENV=production
DEBUG=false

Frontend Configuration

Create .env file in frontend directory:

VITE_API_URL=http://localhost:8000  # For development
# VITE_API_URL=https://your-backend-url.onrender.com  # For production

📦 Dependencies

Backend Requirements

fastapi==0.104.1
uvicorn[standard]==0.24.0
python-multipart==0.0.6
pandas==2.1.3
numpy==1.25.2
scikit-learn==1.3.2
nltk==3.8.1
textblob==0.17.1
requests==2.31.0
python-jose[cryptography]==3.3.0
passlib[bcrypt]==1.7.4

Frontend Dependencies

{
  "dependencies": {
    "react": "^18.2.0",
    "react-dom": "^18.2.0",
    "react-router-dom": "^6.8.0",
    "axios": "^1.6.0",
    "tailwindcss": "^3.3.0",
    "lucide-react": "^0.292.0",
    "chart.js": "^4.4.0",
    "react-chartjs-2": "^5.2.0"
  }
}

🐳 Docker Support

Docker Compose

version: '3.8'
services:
  backend:
    build: ./backend
    ports:
      - "8000:8000"
    environment:
      - FASTAPI_ENV=production
  
  frontend:
    build: ./frontend
    ports:
      - "5173:80"
    depends_on:
      - backend

📊 API Endpoints

Authentication

• POST /api/auth/register - User registration
• POST /api/auth/login - User login

Data Management

• POST /api/upload - Upload CSV data
• GET /api/data-status - Check data status
• GET /api/export - Export analysis results

ML Models

• POST /api/train - Train sentiment model
• POST /api/predict - Make predictions
• GET /api/models - Get available models

Social Media

• GET /api/social/trending - Get trending topics
• POST /api/social/search - Search social media
• POST /api/social/insights - Get topic insights

🎯 Usage

1. Upload Data: Upload CSV files with text data for sentiment analysis
2. Train Models: Choose between Logistic Regression or Naive Bayes
3. View Dashboard: Interactive charts showing sentiment distribution
4. Social Search: Search trending topics and analyze social media sentiment
5. AI Insights: Get intelligent analysis powered by Claude API

🔒 Security

• JWT-based authentication
• CORS protection
• Input validation
• Environment variable management
• Secure API endpoints

📈 Performance

• Async/await for non-blocking operations
• Efficient data processing with Pandas
• Optimized ML model training
• Responsive UI with React
• FastAPI for high-performance API

🐛 Troubleshooting

Common Issues

1. Port conflicts: Change ports in startup scripts
2. Environment variables: Ensure all required variables are set
3. CORS issues: Check frontend API URL configuration
4. Build failures: Verify all dependencies are installed

Debug Mode

Enable debug mode by setting:

DEBUG=true
FASTAPI_ENV=development

🤝 Contributing

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

📄 License

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

📞 Support

For support, please:

• Open an issue on GitHub
• Check the troubleshooting section
• Review the API documentation at /docs

🌟 Live Demo

Check out the live demo: SentimentSync on Render

---

Built with ❤️ using React, FastAPI, and Machine Learning

• Authentication: JWT-based secure login system
• Session History: Track and revisit past analysis sessions

Tech Stack

Frontend

• React 18 with Vite
• TailwindCSS for styling
• Recharts for data visualization
• Axios for API communication

Backend

• FastAPI with Python
• scikit-learn for ML models
• NLTK for NLP preprocessing
• TextBlob for sentiment analysis
• JWT authentication
• Anthropic Claude API integration

Quick Start

Backend Setup

cd backend
pip install -r requirements.txt
uvicorn main:app --reload --port 8000

Frontend Setup

cd frontend
npm install
npm run dev

Access the Application

1. Open your browser and navigate to http://localhost:5173
2. You'll see the Landing Page with "Bird Sound Recognition System" branding
3. Click "Start Detection" to enter the main SentimentSync application
4. Login with credentials: admin / password123
5. Upload your CSV data and start analyzing sentiment!

Project Structure

sentimentsync/
|-- frontend/
|   |-- src/
|   |   |-- pages/          # React pages
|   |   |-- components/     # Reusable components
|   |   |-- App.jsx         # Main app component
|   |   `-- main.jsx        # Entry point
|   |-- package.json
|   `-- vite.config.js
|-- backend/
|   |-- main.py             # FastAPI application
|   |-- nlp_pipeline.py    # NLP preprocessing
|   |-- models.py           # ML models
|   |-- auth.py             # Authentication
|   |-- insights.py         # Claude API integration
|   `-- requirements.txt
`-- README.md

API Endpoints

• POST /api/auth/login - User authentication
• POST /api/upload - CSV file upload and processing
• POST /api/train - Model training endpoint
• GET /api/insights - AI-powered insights
• GET /api/history - Analysis session history
• GET /api/export/{session_id} - Export analyzed data

Default Credentials

• Username: admin
• Password: password123

CSV Format

The app expects CSV files with at minimum a text column. Optional columns:

• date or timestamp - for time-based analysis
• likes or like_count - engagement metrics
• retweets, replies, shares - engagement metrics
• sentiment - pre-labeled sentiment (optional)