SENTIMENT-ANALYZER

Real-Time Sentiment Classification with Emoji Recommendations

Built with the tools and technologies:

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🚀 Live Demo

Try the live application: [Click Here]

Features

• 3-Class Sentiment Analysis: Positive, Negative, Neutral
• Smart Emoji Predictions: Context-aware emoji suggestions
• Real-time Analysis: Instant results with confidence scores
• Modern UI: Clean, responsive Streamlit interface

🏁 Quick Start

1️⃣ Clone & Install

git clone https://github.com/Abhay-Rudatala/Sentiment-Analyzer.git
cd Sentiment-Analyzer
pip install -r requirements.txt

2️⃣ Train Models

python src/training/train_distilbert.py

4️⃣ Run App

streamlit run app/streamlit_app.py

🌐 Open your browser to http://localhost:8501

📁 Project Structure

├── 🎨 app/streamlit_app.py          # Streamlit web application
├── 📁 src/models/                   # Model classes
├── 🤖 src/training/                 # Training scripts
├── 📊 data/processed/               # Processed data files
├── 📦 requirements.txt              # Dependencies
└── 📖 README.md                     # This file

📈 Model Details

• Architecture: DistilBERT (66M parameters)
• Classes: Negative, Neutral, Positive
• Performance: ~85% accuracy on test data

🤝 Contributing

Contributions are welcome! Please feel free to submit a Pull Request.

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

🌟 Show Your Support

If this project helped you, please ⭐ star this repository!

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Ready to analyze your resume? Let's get started! 🚀