lda-project/lda

Health v2 · maintenance only

Flags: no_releases

Adoption not part of the score

1315 stars · 390 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded

A Python library implementing latent Dirichlet allocation (LDA) topic modeling via collapsed Gibbs sampling, with a scikit-learn-style API. It accepts document-term matrices (including sparse) and is fast and cross-platform.

Use cases

• discover topics in a corpus of documents
• run LDA topic modeling on a document-term matrix
• extract topic-word distributions from text data
• cluster news articles by latent themes
• unsupervised text analysis in Python
• fit an LDA model with a scikit-learn-like fit/transform interface

When to choose

• you need classic Gibbs-sampled LDA with a simple scikit-learn-compatible API
• you want a fast, well-tested Python topic modeling library for document-term matrices
• your project needs stable, maintenance-mode behavior without API churn

When to avoid

• you need actively developed features or newer topic modeling methods (e.g., neural topic models, BERTopic)
• you need online/stochastic variational inference for very large streaming corpora
• you need GPU acceleration

Facets

library · maturity maintenance

machine-learning nlp data-science machine-learning data-science python windows cross-platform topic-modeling latent-dirichlet-allocation gibbs-sampling scikit-learn-compatible unsupervised-learning natural-language-processing linux macos

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