Documentation
README
LDA Topic Modeling
Overview
Latent Dirichlet Allocation models each document as a mixture of topics and each topic as a distribution over words. Discovers K latent topics from a corpus without supervision. Uses Gibbs sampling or variational inference. Complexity: O(N × K × iterations) where N = total word tokens.
When to Use
Trigger conditions:
- Discovering latent themes in a large document collection
- Organizing/categorizing documents by automatically discovered topics
- Exploratory text analysis when categories are unknown
When NOT to use:
- When categories are known (use supervised classification)
- For short texts (tweets, titles) — too few words per document for reliable topic assignment
- When you need semantic understanding (use embeddings)
Algorithm
This is the opening of the README. Read the full README on GitHub.