Implementation of various topic models in Python
Python implementations of various topic modeling algorithms including LDA, collaborative topic models, and hierarchical Dirichlet processes.
What is Implementation of various topic models in Python?
python-topic-model is a Python library that implements various topic modeling algorithms for discovering latent thematic structures in text documents. It provides educational implementations of models like Latent Dirichlet Allocation (LDA), collaborative topic models, and hierarchical Dirichlet processes. The project solves the need for accessible, transparent implementations of topic modeling algorithms for learning and research purposes.
Researchers, data scientists, and students who want to understand topic modeling algorithms or need reference implementations for academic projects. It's particularly suitable for those working with text analysis who prefer transparent code over optimized production libraries.
Developers choose python-topic-model for its comprehensive collection of topic modeling implementations in one package and its educational focus with clear, inspectable code. Unlike optimized production libraries, it prioritizes algorithmic transparency and serves as a learning resource for understanding how different topic models work internally.
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Implementation of various topic models
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