NMF Topic Modeling

Overview

Non-negative Matrix Factorization (NMF) decomposes a TF-IDF document-term matrix into two non-negative matrices: W (document-topic) and H (topic-term). The non-negativity constraint produces additive, interpretable topics where each document is a weighted mixture of themes and each theme is a weighted mixture of terms. NMF typically produces more coherent, less overlapping topics than LDA, especially on shorter texts.

When to Use

• Discovering latent themes across a corpus of documents (posts, comments, articles, reviews)
• Finding hidden connections between seemingly unrelated content areas
• Corpus has 100+ documents with substantive text (not just titles or single sentences)
• Need interpretable, distinct topics with minimal overlap
• Working with TF-IDF weighted text (NMF strength vs LDA's bag-of-words)

When NOT to use:

• Corpus has fewer than 50 documents -- too sparse for meaningful factorization
• Documents are extremely short (under 10 words each) without enrichment strategies
• You need probabilistic topic distributions with uncertainty estimates (use LDA instead)
• A single definitive classification is needed (use supervised methods instead)