ThematOS: A Python Library for Topic Modeling

• Documentation: https://thematos.entelecheia.ai
• GitHub: https://github.com/entelecheia/thematos
• PyPI: https://pypi.org/project/thematos

ThematOS is a comprehensive and user-friendly Python library designed for topic modeling, a key technique in natural language processing (NLP) and machine learning (ML) used to extract hidden patterns and themes from large collections of text data. The name ThematOS is derived from the Greek word θέματος (topic), reflecting the library's primary focus on uncovering the underlying structure of textual data.

Features

ThematOS offers a rich set of features that cater to both beginners and experienced practitioners in the field of NLP:

• User-friendly API: ThematOS provides an intuitive API, enabling users to easily create, train, and apply topic models with minimal lines of code.

• Various topic modeling algorithms: The library supports a wide range of state-of-the-art topic modeling techniques, including Latent Dirichlet Allocation (LDA), Non-negative Matrix Factorization (NMF), and Hierarchical Dirichlet Process (HDP), allowing users to explore different methods and choose the most suitable one for their projects.

• Scalability: ThematOS is designed to handle large-scale text corpora efficiently, utilizing advanced algorithms and parallel processing techniques to ensure rapid processing and analysis.

• Customizability: Users can create custom topic models with full control over model parameters, preprocessing steps, and evaluation metrics, tailoring the models to their specific needs.

• Visualization tools: ThematOS includes a variety of visualization tools to help users explore and interpret the results of topic models, such as word clouds, topic distribution charts, and interactive visualizations.

Installation

You can install ThematOS using pip:

pip install thematos

Getting Started

To get started with ThematOS, visit the official documentation and the GitHub repository for examples, tutorials, and more information.

Changelog

See the CHANGELOG for more information.

Contributing

Contributions are welcome! Please see the contributing guidelines for more information.

License

This project is released under the MIT License.