---
title: Sentiment Analysis docker
emoji: 📊
colorFrom: gray
colorTo: gray
sdk: gradio
sdk_version: "5.34.1"
app_file: app.py
pinned: false
license: mit
---


short\_description: sentiment-analysis



\# 🎬 AI Movie Sentiment Analyzer



A sophisticated sentiment analysis application for movie reviews using advanced deep learning techniques with BERT, LIME, and SHAP explanations.



\## Features



\- \*\*Fast Sentiment Analysis\*\*: Quick movie review sentiment classification

\- \*\*Advanced Explanations\*\*: LIME and SHAP-based word importance analysis

\- \*\*Batch Processing\*\*: Analyze multiple reviews simultaneously

\- \*\*Interactive Visualizations\*\*: Charts, gauges, word clouds, and heatmaps

\- \*\*History Tracking\*\*: Keep track of all analyses with trend visualization

\- \*\*Data Export\*\*: Export results in CSV and JSON formats

\- \*\*File Upload Support\*\*: Process CSV and text files

\- \*\*Multiple Themes\*\*: Customizable color themes for visualizations



\## Project Structure



```

sentiment\\\_analyzer/

├── config.py              # Configuration management

├── models.py              # Model loading and management

├── analyzer.py            # Core sentiment analysis logic

├── visualizer.py          # Visualization components

├── utils.py               # Utility functions and data handling

├── app.py                 # Gradio interface and main application

├── requirements.txt       # Python dependencies

├── Dockerfile            # Docker container configuration

├── docker-compose.yml    # Docker Compose setup

└── README.md            # Project documentation

```



\## Installation



\### Local Installation



1\. \*\*Clone the repository\*\*

   ```bash

   git clone <repository-url>

   cd sentiment\_analyzer

   ```



2\. \*\*Create virtual environment\*\*

   ```bash

   python -m venv venv

   source venv/bin/activate  # On Windows: venv\\Scripts\\activate

   ```



3\. \*\*Install dependencies\*\*

   ```bash

   pip install -r requirements.txt

   ```



4\. \*\*Run the application\*\*

   ```bash

   python app.py

   ```



\### Docker Installation



1\. \*\*Using Docker Compose (Recommended)\*\*

   ```bash

   docker-compose up --build

   ```



2\. \*\*Using Docker directly\*\*

   ```bash

   docker build -t sentiment-analyzer .

   docker run -p 7860:7860 sentiment-analyzer

   ```



\## Usage



\### Web Interface



1\. Open your browser and navigate to `http://localhost:7860`

2\. Choose from three main tabs:

   - \*\*Quick Analysis\*\*: Fast sentiment analysis with basic visualizations

   - \*\*Advanced Analysis\*\*: Deep analysis with LIME/SHAP explanations

   - \*\*Batch Analysis\*\*: Process multiple reviews at once



\### API Usage



The application can be extended to provide API endpoints for programmatic access.



\## Configuration



Modify `config.py` to customize:



\- \*\*Model Settings\*\*: Batch sizes, text length limits

\- \*\*Visualization\*\*: Figure sizes, color themes

\- \*\*Processing\*\*: Cache sizes, stop words

\- \*\*History\*\*: Maximum history size



\## Model Information



\- \*\*Base Model\*\*: BERT (entropy25/sentimentanalysis)

\- \*\*Classes\*\*: Positive, Negative

\- \*\*Explanation Methods\*\*: LIME, SHAP

\- \*\*Supported Languages\*\*: English



\## Features Detail



\### Quick Analysis

\- Fast sentiment classification

\- Confidence scoring

\- Probability visualization

\- Word cloud generation



\### Advanced Analysis

\- LIME-based word importance

\- SHAP value calculation

\- Interactive heatmap visualization

\- Detailed explanations



\### Batch Processing

\- CSV/TXT file upload

\- Bulk sentiment analysis

\- Comprehensive result visualization

\- Progress tracking



\### History \& Export

\- Analysis history tracking

\- Trend visualization

\- CSV/JSON export

\- Data persistence



\## Performance



\- \*\*GPU Support\*\*: Automatic CUDA detection

\- \*\*Memory Management\*\*: Efficient batch processing

\- \*\*Caching\*\*: LRU cache for text processing

\- \*\*Resource Optimization\*\*: Context managers for memory cleanup



\## Dependencies



\### Core Dependencies

\- `torch`: Deep learning framework

\- `transformers`: BERT model implementation

\- `gradio`: Web interface framework



\### Analysis \& Visualization

\- `lime`: Local interpretable model explanations

\- `shap`: Shapley additive explanations

\- `matplotlib`: Plotting and visualization

\- `wordcloud`: Word cloud generation



\### Data Processing

\- `pandas`: Data manipulation

\- `numpy`: Numerical computing



\## Development



\### Adding New Features



1\. \*\*New Analyzers\*\*: Add to `analyzer.py`

2\. \*\*Visualizations\*\*: Extend `visualizer.py`

3\. \*\*UI Components\*\*: Modify `app.py`

4\. \*\*Configuration\*\*: Update `config.py`



\### Testing



```bash

\\# Run tests (if implemented)

python -m pytest tests/



\\# Manual testing

python -c "from analyzer import SentimentEngine; engine = SentimentEngine(); print(engine.analyze\\\_single\\\_fast('Great movie!'))"

```



\## Deployment



\### Production Deployment



1\. \*\*Environment Variables\*\*

   ```bash

   export GRADIO\_SERVER\_NAME=0.0.0.0

   export GRADIO\_SERVER\_PORT=7860

   ```



2\. \*\*Resource Requirements\*\*

   - CPU: 2+ cores recommended

   - RAM: 4GB+ recommended

   - GPU: Optional (CUDA support)



3\. \*\*Monitoring\*\*

   - Health checks included in Docker setup

   - Logging configured for production use



\## Troubleshooting



\### Common Issues



1\. \*\*CUDA Out of Memory\*\*

   - Reduce `BATCH\\\_PROCESSING\\\_SIZE` in config

   - Use CPU-only mode



2\. \*\*Model Loading Errors\*\*

   - Check internet connection

   - Verify Hugging Face model availability



3\. \*\*File Processing Issues\*\*

   - Ensure proper file encoding (UTF-8 recommended)

   - Check CSV format and column structure



\### Performance Optimization



\- Use GPU if available

\- Adjust batch sizes based on available memory

\- Enable caching for repeated analyses

\- Use Docker for consistent performance



\## Contributing



1\. Fork the repository

2\. Create a feature branch

3\. Make your changes

4\. Add tests if applicable

5\. Submit a pull request



\## License



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



\## Acknowledgments



\- Hugging Face for BERT model hosting

\- LIME and SHAP libraries for explainable AI

\- Gradio for the intuitive web interface

\- The open-source community for various dependencies



\## Support



For issues and questions:

1\. Check the troubleshooting section

2\. Review existing GitHub issues

3\. Create a new issue with detailed information



\## Changelog



\### v1.0.0

\- Initial release with core functionality

\- BERT-based sentiment analysis

\- LIME and SHAP explanations

\- Gradio web interface

\- Docker support