This repository contains an implementation of Latent Dirichlet Allocation (LDA) for topic modeling using questions sourced from Stack Overflow. The LDA model is trained on the questions dataset to identify the underlying topics discussed by the programming community. Furthermore, the repository provides visualizations to aid in exploring and understanding the topics discovered by the model.
- Preprocessing module for cleaning and preparing the Stack Overflow questions dataset.
- LDA model implementation for topic modeling on the preprocessed dataset.
- Visualizations for analyzing and interpreting the discovered topics.
- Example notebooks demonstrating the usage and showcasing the capabilities of the code.
- Extensive documentation to guide users through the setup and usage of the code.
- Clone the repository:
git clone https://github.com/zaidharis2801/StackOverflow-LDA-TopicModeling-with-Visualizations.git- Install the required dependencies:
pip install -r requirements.txt- Preprocess the Stack Overflow questions dataset using the provided preprocessing module.
- Train the LDA model on the preprocessed dataset to identify the underlying topics.
- Explore and analyze the discovered topics using the visualizations included in the repository.
- Refer to the example notebooks for detailed usage instructions and demonstrations of the code's capabilities.
Contributions are welcome! If you find any issues or have suggestions for improvements, please open an issue or submit a pull request. Make sure to follow the repository's code of conduct.
This project is licensed under the MIT License.
- The Stack Overflow community for providing the valuable dataset used in this project.
