🎯 Sentimeter - Smart Sentiment Analysis Tool

Sentimeter is an intelligent sentiment analysis tool that helps you understand the emotional tone and sentiment behind text data. Whether you're analyzing customer feedback, social media posts, or any other text content, Sentimeter provides accurate and insightful sentiment analysis.

🌟 Features

• Real-time Sentiment Analysis - Analyze text sentiment instantly
• Multi-language Support - Works with various languages
• Batch Processing - Analyze multiple texts at once
• API Integration - Easy-to-use REST API
• Detailed Reports - Get comprehensive sentiment insights
• User-friendly Interface - Simple and intuitive design

🚀 Quick Start

Installation

# Clone the repository
git clone https://github.com/JayeshCC/sentimeter.git
cd sentimeter

# Install dependencies (when available)
# npm install

Basic Usage

# Start the application
# npm start

# Or use via API
curl -X POST http://localhost:3000/analyze \
  -H "Content-Type: application/json" \
  -d '{"text": "I love this product!"}'

📊 How It Works

Sentimeter uses advanced natural language processing to analyze text and determine sentiment. Here's how it works:

1. Text Input - You provide the text to analyze
2. Processing - Our algorithms analyze the text structure and context
3. Sentiment Classification - The text is classified as positive, negative, or neutral
4. Results - You receive detailed sentiment scores and insights

📖 Documentation

• User Manual - Complete guide for end users
• API Documentation - Technical reference for developers
• Examples - Code examples and use cases

🔧 API Overview

Analyze Text Sentiment

POST /api/analyze
Content-Type: application/json

{
  "text": "Your text here",
  "options": {
    "language": "en",
    "detailed": true
  }
}

Response

{
  "sentiment": "positive",
  "confidence": 0.85,
  "scores": {
    "positive": 0.85,
    "negative": 0.10,
    "neutral": 0.05
  },
  "summary": "The text expresses positive sentiment with high confidence."
}

🎯 Use Cases

• Customer Feedback Analysis - Understand customer satisfaction
• Social Media Monitoring - Track brand sentiment
• Product Reviews - Analyze review sentiment automatically
• Market Research - Gauge public opinion
• Content Moderation - Identify negative content

🤝 Contributing

We welcome contributions! Please see our Contributing Guidelines for details.

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

📄 License

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

📞 Support

• 📧 Email: support@sentimeter.com
• 🐛 Issues: GitHub Issues
• 📖 Documentation: Wiki

🙏 Acknowledgments

• Thanks to the open-source community for inspiration
• Built with modern NLP technologies
• Powered by advanced machine learning algorithms

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Made with ❤️ by the Sentimeter Team