This project is a GUI-based Sentiment Analysis tool that classifies text input into positive or negative. It leverages trained machine learning models for logistic regression and TF-IDF vectorization, packaged with a desktop graphical user interface.
- User-friendly GUI for easy input of text data.
- Real-time sentiment classification.
- Uses pre-trained machine learning models for accurate predictions.
- Supports loading and saving of model files.
- Built with Python (Jupyter Notebook for model training) and GUI developed in Visual Studio Code.
sentiment_gui.exe- Executable for the GUI application (handled via Git LFS).best_model_logistic_regression.pkl- Serialized logistic regression model file.tfidf_vectorizer.pkl- Serialized TF-IDF vectorizer file..gitattributes- Git LFS configuration file.
- Clone the repository.
- Ensure Git LFS is installed to handle large files properly.
- Run the GUI executable or launch the Python scripts for training/updating models.
- Input text and receive sentiment analysis results instantly.
- Model training done in Jupyter Notebook.
- GUI developed and maintained in Visual Studio Code.
- Contributions are welcome through pull requests.
MIT License © 2025 Eesa.p.Muhammadali