What is Topic Modeling

How It Works

Topic models analyze word co-occurrence patterns across a document collection to identify groups of words that frequently appear together, representing latent topics. Each document is represented as a mixture of topics, and each topic is a distribution over words. This enables automatic discovery of thematic structure without requiring predefined categories or labeled data.

Technical Details

Classical approaches include Latent Dirichlet Allocation (LDA) using probabilistic generative modeling and Non-negative Matrix Factorization (NMF). Modern neural topic models use variational autoencoders (ProdLDA, ETM) or leverage pretrained embeddings. BERTopic combines sentence embeddings, UMAP dimensionality reduction, and HDBSCAN clustering for state-of-the-art coherent topic discovery. Topic coherence and diversity metrics evaluate model quality.

Best Practices

• Use BERTopic for modern topic modeling that leverages pretrained language model knowledge
• Evaluate topic quality using coherence scores and human interpretability assessments
• Experiment with different numbers of topics and use elbow plots to find the right granularity
• Apply topic models to text extracted from multimodal content (captions, transcripts, OCR) for organization

Common Pitfalls

• Choosing too many or too few topics without systematic evaluation
• Not preprocessing text (removing stop words, normalizing) before applying classical topic models
• Interpreting topics as definitive categories rather than probabilistic clusters
• Applying topic models to very short texts where word co-occurrence patterns are sparse

Advanced Tips

• Use dynamic topic models to track how topics evolve over time in streaming content
• Combine topic assignments with vector search for topic-filtered semantic retrieval
• Apply hierarchical topic modeling to discover topic structures at multiple granularity levels
• Use cross-modal topics that combine visual and textual themes in multimodal collections