🎯 Sentiment Analysis App

A full-stack sentiment analysis application that analyzes the emotional tone and sentiment of text content from multiple sources. Built with React, Express, and Google Cloud APIs for production-ready performance.

Status: In active development — local setup instructions below | GitHub: StreetCoder02/Sentiment-Analysis-App

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✨ Features

• 📝 Multi-Input Support - Analyze sentiment from:

  • Direct text input
  • File uploads (PDF, TXT, DOCX, Images with OCR)
  • URL/web page content
  • Batch processing (up to 50 texts)

• 🌐 Multi-Language Support

  • Automatic language detection
  • Auto-translation to English for analysis
  • Supports 15+ languages

• 🤖 AI-Powered Insights

  • Google Cloud Natural Language API for sentiment analysis
  • Google Gemini AI for intelligent summary generation
  • Confidence scores and detailed breakdowns

• 📊 Batch Analysis

  • Process multiple texts simultaneously
  • Export results as CSV for further analysis
  • Real-time progress tracking

• 👤 Authentication

  • Firebase authentication
  • User analytics and history tracking
  • Demo mode for testing without login

• 📱 Responsive UI

  • Beautiful, modern interface with Framer Motion animations
  • TailwindCSS styling
  • Radix UI components
  • Mobile-friendly design

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🛠 Tech Stack

Frontend

• React 18 - UI framework
• React Router 6 - SPA routing
• TypeScript - Type safety
• Vite - Fast build tool
• TailwindCSS 3 - Styling
• Framer Motion - Animations
• Radix UI - Component library
• Lucide React - Icons

Backend

• Express.js - REST API server
• Node.js - Runtime
• Google Cloud Natural Language API - Sentiment analysis
• Google Gemini AI - AI summaries
• Multer - File uploads
• Tesseract OCR - Image text extraction

Services

• Firebase - Auth, Firestore, Storage
• Google Cloud - NLP services
• Netlify Functions - Serverless deployment

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📋 Prerequisites

• Node.js v18 or higher
• npm or pnpm
• Firebase account (for backend services)
• Google Cloud API keys (optional, for full features)

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🚀 Quick Start

1. Clone the Repository

git clone https://github.com/StreetCoder02/Sentiment-Analysis-App.git
cd Sentiment-Analysis-App

2. Install Dependencies

npm install
# or
pnpm install

3. Setup Environment Variables

# Copy the example environment file
cp .env.example .env

# Edit .env and add your API keys:
# - VITE_FIREBASE_API_KEY
# - VITE_FIREBASE_PROJECT_ID
# - GOOGLE_CLOUD_API_KEY (optional)
# - GEMINI_API_KEY (optional)

4. Start Development Server

npm run dev

The app will be available at:

• Frontend: http://localhost:5173
• API: http://localhost:8080

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📦 Project Structure

Sentiment-Analysis-App/
├── client/                 # React frontend
│   ├── pages/             # Route pages (Index, BatchAnalysis, Login)
│   ├── components/        # React components
│   │   └── ui/            # Radix UI components
│   ├── hooks/             # Custom React hooks
│   ├── lib/               # Utilities (Firebase, Auth)
│   └── global.css         # TailwindCSS styles
├── server/                # Express API backend
│   ├── routes/            # API endpoints
│   └── utils/             # Sentiment analysis utilities
├── shared/                # Shared TypeScript interfaces
├── netlify/functions/     # Serverless functions
├── public/                # Static assets
└── package.json           # Dependencies

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🔌 API Endpoints

Sentiment Analysis

• POST /api/sentiment/analyze - Analyze single text
• POST /api/sentiment/batch - Analyze multiple texts
• POST /api/sentiment/file - Analyze uploaded file
• POST /api/sentiment/url - Analyze URL content
• POST /api/sentiment/translate - Translate text

Demo

• GET /api/demo - Test endpoint
• GET /api/ping - Health check

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🎨 Features in Detail

Text Analysis

Enter any text and get:

• Sentiment classification (Positive/Negative/Neutral)
• Confidence scores
• Detailed breakdown of sentiment components
• AI-generated insights

File Upload

Supports:

• PDF - Text extraction
• TXT - Plain text
• DOCX - Word documents
• Images - OCR text extraction

URL Analysis

• Fetch and analyze web page content
• Automatic language detection
• Full sentiment analysis pipeline

Batch Processing

• Upload CSV or text file with multiple texts
• Process up to 50 texts at once
• Export results as CSV with all details
• Real-time progress tracking

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🔑 Environment Variables

Create a .env file in the root directory (use .env.example as template):

# Firebase Configuration
VITE_FIREBASE_API_KEY=your_key_here
VITE_FIREBASE_AUTH_DOMAIN=your_domain_here
VITE_FIREBASE_PROJECT_ID=your_project_id_here
VITE_FIREBASE_STORAGE_BUCKET=your_bucket_here
VITE_FIREBASE_MESSAGING_SENDER_ID=your_id_here
VITE_FIREBASE_APP_ID=your_app_id_here
VITE_FIREBASE_MEASUREMENT_ID=your_measurement_id_here

# Google Cloud APIs
GOOGLE_CLOUD_API_KEY=your_api_key_here

# Gemini AI (Optional)
GEMINI_API_KEY=your_gemini_key_here

# Server Config
NODE_ENV=development
PORT=3001

How to get API keys:

• Firebase Console
• Google Cloud Console
• Google Gemini API

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📚 Available Commands

# Development
npm run dev              # Start dev server (client + API)
npm run dev:api         # Start API only
npm run dev:full        # Start with API and client separately

# Production
npm run build           # Build for production
npm run build:client    # Build frontend only
npm run build:server    # Build backend only
npm start               # Start production server

# Testing & Quality
npm run test            # Run tests with Vitest
npm run typecheck       # TypeScript validation
npm run format.fix      # Auto-format code

# Firebase
npm run emulators       # Start Firebase emulators
npm run setup-firebase  # Setup Firebase project

# Utilities
npm run status          # Check project status
npm run test-api        # Test API endpoints

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🚢 Deployment

Netlify (Recommended)

# Build
npm run build

# Deploy
# Use Netlify CLI or connect GitHub for auto-deploy

Vercel

# Deploy directly via Vercel dashboard
# Connect your GitHub repository

Docker

docker build -t sentiment-app .
docker run -p 8080:8080 sentiment-app

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🔐 Security

• ✅ Environment variables for sensitive data
• ✅ No API keys in source code
• ✅ Firebase security rules for data access
• ✅ CORS protection
• ✅ Input validation and sanitization

Important: Never commit .env files to version control. Use .env.example as a template.

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🤝 Contributing

Contributions are welcome! Here's how:

1. Fork the repository
2. Create a feature branch (git checkout -b feature/AmazingFeature)
3. Commit changes (git commit -m 'Add AmazingFeature')
4. Push to branch (git push origin feature/AmazingFeature)
5. Open a Pull Request

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📝 License

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

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🙋 Support

• 📧 Email: apsinghjp0516@gmail.com
• 🐛 Issues: GitHub Issues
• 💬 Discussions: GitHub Discussions

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🎓 Learning Resources

This project demonstrates:

• Full-stack development with React and Express
• Integration with Google Cloud APIs
• Firebase authentication and database
• TypeScript best practices
• Responsive UI design with TailwindCSS
• Vite build optimization
• Sentiment analysis implementation

Perfect for:

• Portfolio projects
• Learning full-stack development
• Understanding NLP integration
• Building production-ready apps

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🙏 Acknowledgments

• Google Cloud Natural Language API
• Google Gemini AI
• Firebase
• React and Vite communities
• Radix UI and TailwindCSS

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Made with ❤️ by Aniruddha

⭐ If you find this project useful, please give it a star!