Sentiment Analysis Project

A comprehensive Natural Language Processing (NLP) project for sentiment analysis using both traditional machine learning and deep learning approaches.

🌟 Features

• Dual model implementation:
  • Traditional ML: TF-IDF + Logistic Regression
  • Deep Learning: LSTM with word embeddings
• Interactive API endpoints using FastAPI
• Comprehensive visualization tools
• Extensive data preprocessing pipeline
• Model performance comparison utilities

🚀 Quick Start

1. Clone the repository:

git clone https://github.com/gitarber/sentiment-analysis
cd sentiment-analysis

2. Install dependencies:

pip install -r requirements.txt

3. Download and prepare the dataset:

python src/data/download_data.py

4. Train the models:

python src/train_models.py

5. Start the API server:

python src/run_api.py

📊 Project Structure

sentiment-analysis/
├── data/                  # Data directory
│   ├── raw/              # Raw data files
│   └── processed/        # Processed data files
├── models/               # Saved model files
├── notebooks/           # Jupyter notebooks
├── src/                 # Source code
│   ├── api/            # API implementation
│   ├── data/           # Data processing scripts
│   ├── models/         # Model implementations
│   └── visualization/  # Visualization utilities
├── tests/              # Test files
├── .gitignore         # Git ignore file
├── requirements.txt   # Project dependencies
└── README.md         # Project documentation

📓 Notebooks

• notebooks/1_Data_Exploration.ipynb: Dataset analysis and preprocessing steps
• notebooks/2_Model_Training.ipynb: Model training and evaluation process
• notebooks/3_Model_Analysis.ipynb: Detailed model performance analysis
• notebooks/4_API_Usage.ipynb: Examples of using the API endpoints

🛠️ API Endpoints

• /: Welcome message
• /health: Health check
• /analyze/traditional: Analyze sentiment using traditional ML
• /analyze/lstm: Analyze sentiment using LSTM
• /analyze/batch/traditional: Batch analysis with traditional ML
• /analyze/batch/lstm: Batch analysis with LSTM
• /models/info: Get model information

📈 Visualization Tools

• Confusion matrices
• Training history plots
• Model comparison charts
• Feature importance analysis
• Attention weights visualization (for LSTM)

📸 Screenshots

Model Performance

API Interface

Visualization Examples

🧪 Testing

Run the test suite:

pytest tests/

📝 License

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

🤝 Contributing

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