- Text Emotion Detection: The model can analyze and classify emotions from textual input.
- Train and fine-tune machine learning models to achieve high accuracy in emotion detection across different input types.
- Optimize the models for speed and efficiency to allow real-time or near real-time emotion detection.
- Encourage collaboration and contributions from the open-source community to enhance the accuracy and capabilities of the emotion detection system.
- Fork the repository and clone it to your local machine.
- Choose an area of interest (text emotion detection or frontend web page) and propose improvements or optimizations.
- Implement new features, fix bugs, or enhance the existing functionality. Ensure to follow the coding standards and guidelines.
- Submit a pull request with a clear description of your changes and improvements.