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