Topic Modeling
Finding themes no one labeled
Topic models discover latent themes across a corpus: LDA assigns documents to topic mixtures and topics to word distributions. They reveal structure in unlabeled collections and power exploratory NLP.
What you will learn
- Explain LDA's generative story for topics
- Pick the topic count and inspect quality
- Use topic mixtures as features and for exploration
What is this?
Topic modeling finds latent themes in an unlabeled corpus. Each document is a mixture of topics, each topic is a distribution over words. LDA (Latent Dirichlet Allocation) places Dirichlet priors over both and infers the hidden assignment per word via Gibbs/Variational estimates. Result: per-document topic vectors and human-readable word distributions — structure discovery and a dimensionality reduction for downstream models in one.
Why do you need it?
- Unlabeled corpora are the majority — topic models structure them cheaply.
- Topic mixtures make compact, interpretable document features for clustering or classification.
- Exploration (what themes dominate a support log? which topics spike?) drives triage and dashboards.
- They expose drift: topic prevalence changing month-to-month surfaces product shifts early.
Mental model
A librarian shelving every book by the fractions of genre they mix
LDA
A sci-fi-romance isn't one genre or the other — it's 60% sci-fi, 40% romance. LDA similarly assigns each document fractions across hidden topics and lets you read each topic's strongest words as the shelf label.
How it works
- Choose K topics and set priors (α per doc-topic, η per topic-word).
- Initialize random assignments of every word to a topic.
- Gibbs/Variational inference updates each word's topic given the current counts of other words.
- Converge to posterior: doc-topic proportions θ and topic-word distributions φ.
- Inspect topic top-words, assign interpretable labels, and use θ as document features.
Corpus → topic-word distributions → doc mixtures.
In production
- A product research team runs LDA over 50k feedback reviews; the 'battery life' topic's rising share alerted them to a regression.
- A legal discovery system groups contract clauses into topic clusters for review routing.
- An email platform summarizes mail themes with topic mixtures to auto-tag folders.
Best practices
- Tune K with interpretability + held-out coherence, not just perplexity (perplexity praises the vacuous).
- Filter duplicates and boilerplate; LDA is sensitive to near-duplicate docs.
- Iterate: label topics, then merge/split based on overlap.
- Use negative binning in preprocessing (drop ultra-frequent max_df terms) for sharper topics.
Common mistakes
- Believing LDA 'is' a clustering of documents — it's a mixture model; mixing and overlap are the norm.
- Trusting a single seed: LDA is non-deterministic; run several seeds and compare stability.
- Reading topic labels from top-words alone without sampling actual documents.
- Deploying monthly topic models with drift — the same 'topic 4' means different things across retrains.
Common issues
All documents load onto one topicK too small, corpus dominated by boilerplate, or α large and flat
Raise K, filter boilerplate tokens, and lower α a touch; check per-doc entropy.
Topics stable in name, drifting in meaning across retrainsTopic alignment is arbitrary between runs
Align topics by word-overlap across runs or pin initial topics; report drift as the change in θ, not 'topic 4 grew'.
Debugging tips
- A topic whose top words are all function words? Preprocessing leaked noise — filter stopwords/boilerplate harder.
- Documents assigned with near-100% to a single topic each = the model found clusters, good sign.
- Cosmetic: Python LDA with max_iter too low under-mixes; raise iterations and check θ smoothness.
- Coherence up, interpretability down → the metric is gaming you; validate with humans on samples.
Interview checkpoint
Hands-on lab
- Fit LDA over a small synthetic review corpus
- Inspect topic-word distributions and label topics
- Measure topic stability across seeds
Discover Topics in a Review Corpus
30 min · intermediate
Quick quiz
LDA models each document as:
Perplexity alone is a reliable guide for choosing the topic count.