Topic Modeling Basics

Why this matters

As an NLP Engineer, topic modeling helps you quickly surface themes in large text collections. You will use it to:

• Explore unlabeled corpora to guide data cleaning and labeling strategy.
• Generate human-readable topic summaries for reports and stakeholders.
• Create document-topic features that boost classical classifiers or clustering.
• Support recommendation, search, and content tagging by mapping documents to themes.

Who this is for

• Beginners to intermediate NLP practitioners who know basic text preprocessing.
• Engineers needing practical, explainable features from unlabeled text.

Prerequisites

• Basics of text preprocessing: tokenization, lowercasing, stopword handling, lemmatization.
• Vectorization: bag-of-words and TF–IDF.
• Familiarity with linear algebra concepts (matrices) is helpful but not mandatory.

Concept explained simply

Topic modeling is an unsupervised way to discover groups of words that frequently appear together across a collection of documents. Each group is a topic, and each document is a mix of topics. You get:

• Topic → word distribution (which words define each topic)
• Document → topic distribution (which topics define each document)

Common approaches:

• LSA (Latent Semantic Analysis): uses SVD on a term-document matrix (often TF–IDF). Fast, linear algebra based.
• LDA (Latent Dirichlet Allocation): probabilistic model with priors controlling sparsity of topics and words.
• NMF (Non-negative Matrix Factorization): factorizes non-negative matrices (e.g., TF–IDF) into parts-based topics.

Mental model in 60 seconds

Imagine a bookstore. Each shelf (topic) is defined by the types of books (words) on it. Each book (document) sits on multiple shelves depending on what it covers. Topic modeling finds these shelves and how much each book belongs to each shelf by looking at which words co-occur across many books.

Core workflow and key parameters

1. Collect and clean text: remove boilerplate, normalize casing, handle stopwords, and optionally lemmatize.
2. Vectorize: build a term-document matrix with bag-of-words or TF–IDF.
3. Choose a model: LSA (SVD), LDA (probabilistic), or NMF (parts-based) depending on needs.
4. Pick K (number of topics): test a small grid (e.g., 5–30) and evaluate.
5. Fit: train the model on the vectors.
6. Inspect: view top words per topic and representative documents.
7. Label topics: assign short human-readable names for downstream use.
8. Evaluate: coherence (human interpretability), topic distinctiveness, and for LDA optionally perplexity.
9. Use features: extract document-topic vectors as features for downstream tasks.

Parameter cheat sheet

• K: number of topics. Too low = mixed topics; too high = fragmented topics.
• LDA priors: alpha (document-topic sparsity), beta/eta (topic-word sparsity). Smaller values encourage sparser distributions.
• NMF: initialization (e.g., NNDSVD), max iterations, regularization.
• LSA: number of singular components; often built on TF–IDF.
• Preprocessing: min_df/max_df thresholds, n-grams, keep domain-specific tokens.

Worked examples

Example 1 — Quick LSA on support tickets

1. Preprocess: lowercase, remove boilerplate signatures, keep bigrams.
2. Vectorize: TF–IDF with min_df=5, max_df=0.7.
3. Apply LSA with K=10 (SVD truncated to 10 components).
4. Inspect top words per component; label topics (e.g., "payment failures", "password reset").
5. Use the 10-dim document vectors as features for a ticket routing classifier.

Example 2 — LDA for product reviews

1. Preprocess: remove brand boilerplate, lemmatize nouns/verbs, keep stopwords minimal to preserve meaning.
2. Vectorize: bag-of-words, min_df=3.
3. Grid search K in {5, 10, 15}. For each, train LDA with alpha=0.1, eta=0.01.
4. Evaluate using topic coherence and manual inspection of top-10 words.
5. Pick K giving coherent, distinct topics. Export document-topic distributions for segmentation.

Example 3 — NMF for news articles (parts-based)

1. Preprocess: TF–IDF with unigrams+bigrams, min_df=10.
2. Train NMF with K=12, init=NNDSVD, max_iter=400.
3. Topics often form additive "parts" like "stock|market", "climate|policy".
4. Map each article to its strongest topics and build a topic heatmap dashboard for editors.

Evaluation and sanity checks

• Coherence: do the top words per topic make human sense?
• Distinctiveness: low overlap of top words across topics.
• Stability: rerun with different random seeds; topics should be similar.
• Usefulness: do document-topic features improve your downstream task?

Exercises you can do now

These exercises mirror the tasks below and can be completed with any standard NLP toolkit. If you cannot run code, perform the steps conceptually and write down your reasoning.

1. Exercise 1: Estimate a good K (number of topics) for a small corpus. See details in the Exercises section below.
2. Exercise 2: Build and interpret a tiny topic model and extract features for a simple classifier.

Self-check checklist

• I compared at least 3 values of K and wrote a short rationale for my choice.
• I inspected top words and representative documents for each topic.
• I labeled topics with short, human-readable names.
• I extracted document-topic vectors and verified their shape matches my corpus size x K.

Common mistakes and how to self-check

• Over-removing words: Aggressive stopword lists or stemming can delete meaning. Self-check: are critical domain terms missing from top words?
• Too many topics: Leads to near-duplicate topics. Self-check: measure overlap of top-10 words; high overlap suggests K is too high.
• No validation: Relying only on automatic scores. Self-check: always do a 5–10 minute manual inspection.
• Ignoring randomness: Not setting seeds or repeating runs. Self-check: rerun and compare topic similarity.
• Forcing the wrong model: Using LSA when interpretability is critical. Self-check: if topics look mixed, try LDA/NMF.

Practical projects

• Customer Feedback Mapper: Fit LDA on 3 months of feedback. Deliver a weekly topic trend chart and top tickets for each topic.
• Newsroom Topic Radar: NMF on recent articles with bigrams; build a dashboard of topic proportions per section (business, tech, sports).
• Internal Docs Organizer: LSA on engineering docs; assign document-topic tags to improve search filters.

Learning path

1. Refresh preprocessing and TF–IDF.
2. Learn the three models (LSA, LDA, NMF) and when to use each.
3. Practice choosing K and evaluating coherence.
4. Export document-topic features and integrate with a classifier.
5. Automate a small pipeline and add monitoring for topic drift.

Next steps

• Try adding bigrams/trigrams to improve interpretability.
• Compare LDA vs NMF on the same corpus and document your findings.
• Wire topic features into a baseline classifier and measure lift.

Mini challenge

Pick a public domain corpus (e.g., 2,000–5,000 articles or reviews). Train two models (LDA and NMF) with the same K. In one page, compare:

• Top-10 words per topic and your labels.
• Topic overlap and coherence (qualitative).
• Lift in F1 when using document-topic features in a simple classifier vs no topic features.

Quick Test info

There is a short, self-check Quick Test for this subskill. Anyone can take it for free. If you are logged in, your progress and best score will be saved.