Did you know? A school website may contain thousands of articles, including lessons about fractions, reading comprehension, chemistry experiments and examinations. A human could read every page and organise the collection, but that takes time. Certain AI methods can help identify recurring themes automatically.
One family of methods is called topic modelling. Instead of requiring a complete list of human-made labels for every document, a topic model looks for recurring patterns in words or other representations and offers groups of related themes.
Quick answer: collect a permitted document set → clean and represent the text → identify recurring word or semantic patterns → estimate topics and document associations → give the topics readable names → check whether they are genuinely useful.
The crucial distinction is that a topic produced by an algorithm is not automatically a curriculum subject, a verified concept or a perfect summary.
The 30-second guide
- Document corpus: the collection of texts being analysed.
- Topic: a recurring pattern of terms or semantics identified under a chosen model.
- Document–term matrix: a numerical table describing which words appear in which documents.
- TF-IDF: a weighting scheme emphasising terms based on frequency within a document and rarity across a corpus.
- LDA: latent Dirichlet allocation, a probabilistic model that represents documents as mixtures of topics.
- NMF: non-negative matrix factorisation, which can extract additive topic-like components from non-negative document features.
- Topic coherence: a family of measures investigating whether the terms grouped into a topic make sense together.
Topics may overlap. A document about mathematical vocabulary could belong partly to a Mathematics theme and partly to a language-learning theme. Forcing every article into exactly one box can erase useful relationships.
Imagine a fictional 12-article library
Suppose our toy collection contains twelve harmless short articles. Four discuss fractions, decimals and equations; four discuss plants, sunlight and photosynthesis; four discuss verbs, sentences and composition.
A reader can see three broad subjects. An appropriately trained topic model might also identify three recurring term patterns that resemble those subjects.
But if one article explains the vocabulary of photosynthesis, it could contain terms from Science and English learning. A good topic system may recognise a mixed case rather than pretend the boundaries are absolute.
Step 1: Decide what the documents represent
Before applying a model, define the purpose. Are the documents school lessons, research abstracts, customer-support messages or news articles? The text length, vocabulary and intended categories can differ substantially.
A topic model that finds broad science themes in thousands of abstracts may not produce meaningful results on ten one-line messages.
Also check the right to process the texts. Private student submissions, confidential business documents and identifiable records need appropriate permission and safeguards.
Step 2: Prepare the text carefully
Common preparation may include handling punctuation, normalising selected variants, examining repeated boilerplate and choosing whether certain frequent words should have less influence.
Terms such as “the” and “is” may appear across almost every article. If the task is to discover distinctive subjects, they often provide less useful differentiation than “fractions” or “photosynthesis.”
But removing words aggressively can damage meaning. Negations, code identifiers, subject names and multilingual expressions sometimes carry crucial information. A single universal cleaning rule is not suitable for every corpus.
Step 3: Turn documents into numerical features
A document–term matrix has one row per document and one column per selected term. Each cell represents a defined word-based measurement, such as a count or TF-IDF weight.
Imagine three fictional articles and two tracked words, “algebra” and “plants.” Article A contains algebra three times and plants zero times. Article B contains algebra once and plants once. Article C contains algebra zero times and plants four times.
A simple count matrix is therefore A = (3, 0), B = (1, 1), C = (0, 4). These numbers preserve some subject cues, but not the whole meaning of each article.
Step 4: Understand TF-IDF
TF-IDF combines information about a term’s frequency within a document with how widespread the term is across the corpus.
The aim is not simply to count which word is most common. A term appearing in nearly every article may tell us less about one article’s distinctive subject than a less common topic word.
TF-IDF is useful for certain topic-extraction methods such as non-negative matrix factorisation, but it does not by itself know that “equation” and “algebraic expression” are related concepts.
Step 5: Discover topics using non-negative matrix factorisation
NMF factorises a non-negative numerical matrix into smaller non-negative factors. For topic modelling, those factors can describe mixtures of topics across documents and the importance of words within topics.
A topic might contain high weights for “fraction,” “decimal,” “numerator” and “denominator.” A human might name it Fractions and Number Sense.
But the model has not discovered an official curriculum heading. It has found a component in its chosen mathematical representation.
The scikit-learn NMF and LDA topic-extraction example demonstrates the technique on a collection of news documents and displays topics through their highest-weighted terms.
Step 6: Learn how LDA represents documents
Latent Dirichlet allocation (LDA) is a probabilistic topic model. In a simplified explanation, each document is treated as a mixture of topics, and each topic is associated with a distribution over terms.
An invented study article might be associated with 60% a Mathematics topic, 30% an English-language topic and 10% a study-skills topic under one fitted LDA model.
Those percentages describe the model’s inferred mixture under its assumptions. They are not verified proportions of the article’s educational value and should not be presented as proof that a specific sentence belongs to a topic.
Step 7: Give discovered topics readable labels
Topic models can output term groups without useful human-readable names. A person may see “fraction, numerator, divide, decimal” and decide that “Fractions” is a suitable editorial label.
The labelling step matters. A top-word list that combines “class, lesson, mathematics, reading” may reflect broad educational boilerplate rather than a clear narrow subject.
A trustworthy workflow shows the important terms and representative documents before naming the topic. If there is no coherent meaning, do not force a sophisticated-sounding title.
A topic label is an interpretation of model output. It is not a newly established fact about every document in the group.
Step 8: Why choosing the number of topics is difficult
Suppose our toy collection has Science, English and Mathematics articles. A model asked for only two topics might combine subjects. A model asked for twenty topics might fragment the same small collection into unstable themes.
Some methods require the analyst to choose a topic count in advance. Others discover or adjust topic groups differently.
The useful choice depends on the collection size, diversity and intended navigation. Coherence measures and human review can help, but there is no universal perfect number.
Step 9: Distinguish topic models from clustering
Clustering groups examples based on a similarity representation. A topic model such as LDA can assign a mixture of topic probabilities to each document.
These techniques sometimes overlap in practical systems, but they do not answer exactly the same question. One article can express several topics even if an ordinary clustering procedure assigns it to one principal cluster.
For the general idea, read How AI Finds Patterns Without Labels.
Step 10: Where embeddings fit
Modern text models can turn documents or paragraphs into embeddings that capture aspects of their semantic relationships. Applications can cluster or otherwise group those embeddings, then extract representative words for topic descriptions.
This can help when two passages use different vocabulary to discuss the same concept. For example, “how plants make food” and “photosynthesis” could be semantically related without sharing all the same words.
Approaches such as BERTopic combine embeddings, clustering and topic-word representations in a flexible workflow. Their results still depend on the embedding model, grouping settings and the document collection.
Step 11: Why top words can mislead
A word may occur repeatedly because it appears in a standard navigation menu, disclaimer or page footer. If boilerplate is left in every document, the model may find a topic that represents the website template rather than the educational content.
Similarly, a very long article can contribute more terms than a short note under some representations.
Inspect preprocessing, source formatting and representative examples. A topic that looks sensible in a table may still be driven by irrelevant technical details.
Step 12: Multilingual collections need special care
A Singapore education site may contain English, Chinese, Malay or Tamil content. Ordinary word tokenisation and stop-word lists designed for one language may not suit the others.
A multilingual embedding model can be useful for some tasks, but that does not automatically guarantee accurate topic assignments across language varieties.
Test meaningful examples in the actual languages, scripts and specialised subject vocabulary the readers use.
Step 13: How to check a topic is useful
Look at several documents strongly associated with a topic. Do they share a recognisable subject? Are important articles missing? Are unrelated documents being grouped together?
An editor can compare the result with a small, human-reviewed sample. Another option is to measure whether the discovered topics improve tasks such as article navigation or document retrieval.
Do not report a topic-quality score as though it measures factual correctness or suitability for every reader.
Step 14: What happens when new documents arrive?
Topics can change as a collection grows. New subjects appear, terminology evolves and earlier labels may become too broad.
A model fitted on an older collection may need updating or an appropriate method of applying its existing topics to new documents. Rebuilding the topics can also shift identifiers, breaking links if they were treated as permanent categories.
This is especially important on a large publishing site. Stable editorial labels and algorithmic topic estimates should not be treated as interchangeable.
Step 15: Topic modelling versus search
A search engine retrieves documents relevant to a particular question. Topic modelling tries to describe recurring themes in the collection as a whole.
They complement one another. Topic results can support browsing, while a retrieval and reranking pipeline can help answer a precise question.
The companion How AI Reranks Search Results for Better Answers explains how the relevance of individual search results can be judged.
Try it: create topics by hand
Write six harmless article titles:
- Adding Fractions
- Solving Equations
- Fractions in Daily Life
- Photosynthesis Explained
- Plants and Sunlight
- How Leaves Make Food
Circle the terms that recur or express related ideas. Write two candidate topic names and consider which article belongs in which group.
Now add “Vocabulary for Explaining Photosynthesis.” This title spans science content and language learning. Does one category capture its value, or do multiple tags make more sense?
The exercise illustrates why the model’s topic count and the site’s editorial categories are choices rather than objective laws.
Five common mistakes
- “Topic models discover the official truth about subjects.” They estimate patterns under a model.
- “Every document has exactly one topic.” Some approaches explicitly allow mixtures.
- “The top words always describe the intended subject.” Boilerplate and common vocabulary can dominate.
- “More topics always mean greater accuracy.” Too many topics can fragment meaning.
- “Topics automatically stay stable as the website changes.” Models and collections evolve.
Frequently asked questions
What is topic modelling?
It is a family of methods that estimates recurring themes or term patterns across a collection of documents.
What does LDA mean in this context?
Latent Dirichlet allocation, a probabilistic topic model. It is not linear discriminant analysis, which shares the same abbreviation in another field.
Does NMF understand a document’s meaning?
NMF factorises a numerical feature matrix. Its components can support topic interpretation, but they are not human understanding or factual verification.
Can one document belong to multiple topics?
Yes. Some models represent documents as mixtures, and practical content may genuinely span subjects.
Can topic modelling replace human editorial organisation?
It can assist discovery and navigation, but stable categories, correct subject labels and important decisions still benefit from review.
Your one-minute takeaway
Topic modelling helps explore large document collections by discovering repeated patterns. NMF, LDA and embedding-based approaches offer different ways to find useful themes. Their outputs are suggestions for organising information, not verified facts about every article. Good results need careful preprocessing, representative examples and human interpretation.
Trusted reading and connected articles
- scikit-learn: Topic Extraction With NMF and LDA — an official worked example.
- scikit-learn: Latent Dirichlet Allocation — probabilistic topic modelling.
- scikit-learn: Non-Negative Matrix Factorisation — additive components.
- BERTopic Documentation — embeddings, clustering and interpretable topics.
- How AI Finds Patterns Without Labels — comparing clustering with topics.
- How Embeddings Find Similar Meanings — semantic representations.
- How AI Finds Answers in Your Documents — navigating from topics to evidence.
- How AI Works: Complete Overview — explore the whole series.