Topic Modeling

Topic modelling is an unsupervised technique for discovering the themes (topics) that run through a large collection of unstructured text, without labelling anything in advance. It is a form of clustering applied to text, and MLforSEO calls it the flagship clustering use case in SEO: making sense of blogs, product descriptions, reviews and queries at a scale no one could read.

How it works

All approaches turn text into numbers, group what is similar and describe each group with representative words. The main families are:

• Probabilistic models such as LDA, which treat each document as a mixture of topics.
• Matrix factorisation such as LSA and NMF, which decompose a word-document matrix.
• Embedding-based models such as BERTopic and Top2Vec, which embed documents, cluster them and label clusters (BERTopic uses c-TF-IDF).

Results are judged by reading the topics and with metrics such as the coherence score. Because it is exploratory, expect to run it several times, adjusting the number of topics, pre-processing and model choices.

How it’s used in SEO and marketing

• Content audits: map what a site actually covers, find thin or overlapping areas and plan hubs.
• Competitor gap analysis: run the same model on your content and a competitor’s.
• Internal linking: link pages that share topics.
• Voice of customer: themes in reviews, support tickets, surveys and social posts.
• Keyword research: group large query sets into themes.

Example

MLforSEO’s BERTopic guide runs a head-to-head test of BERTopic, LDA and NMF on page titles only, scored with the same coherence metric, and explains eight reasons short text is hard for every topic model (few words per document, sparse co-occurrence, noise and so on) with a fix for each. The practical takeaway: for titles, queries and reviews, start with an embedding-based model; for long-form content, LDA remains a transparent, well-understood option.

Limitations and things to watch

• Topics come out as word lists; a human or LLM still has to name and sanity-check them.
• Pre-processing, the number of topics and random seeds can change results considerably.
• Short texts and small datasets give unstable topics.
• Mixed or irrelevant data (navigation text, boilerplate, duplicate replies) pollutes topics, so clean and de-duplicate first.
• Hard clustering assigns one topic per document; use topic distributions when documents cover several themes.

Learn it on MLforSEO

• Topic Modelling for SEO with LDA (Latent Dirichlet Allocation)
• Topic Modelling for SEO with BERTopic
• What Machine Learning Clustering Is, and Where It Fits in SEO
• BERTopic: Benefits and Limitations
• Introduction to Machine Learning for SEO (course)