LLMs & AI Agents · Text analytics
How Does a Computer Know What a Text Is About? Topic Modeling for Companies
Customer feedback, support tickets, contracts, policy papers: companies hold far more text than anyone can read. Topic modeling finds the recurring themes in these collections automatically and shows what people are talking about, without reading every document first.
Key takeaways
- Topic modeling is an unsupervised machine learning method that groups documents by the themes that recur in them.
- The classic method, latent Dirichlet allocation (LDA), treats each document as a mix of topics and each topic as a mix of words.
- Newer methods group texts by meaning using embeddings and let language models name the topics, which gives more readable results.
- Companies use topic modeling to analyse feedback and tickets, monitor media and policy, detect trends and improve search.
What topic modeling is
Natural language processing (NLP) combines machine learning and linguistics to analyse text automatically. One of its most useful applications is topic modeling: discovering the abstract themes that appear across a large collection of documents.
Topic modeling is unsupervised machine learning. Nobody has to define the categories in advance or label examples. The method finds hidden structures by grouping words and documents that belong together. For a human, reading thousands of documents and drawing consistent conclusions would take far too long. Topic modeling produces that overview in a fraction of the time.
Where topic modeling pays off for companies
- Customer feedback and support: find out what reviews, surveys and tickets are really about, and which issues are growing.
- Media and social media monitoring: see which themes dominate the conversation about a brand, a product or an event.
- Policy and regulatory monitoring: structure large volumes of documents from institutions and spot relevant themes early.
- Search and recommendations: group related documents so users find what fits their question.
- Trend detection: track how the weight of topics shifts over time.
In practice: Policy-Insider.AI
Public affairs teams used to track political documents across institutions and languages by hand. For Policy-Insider.AI we built a platform that collects, structures and analyses millions of political documents in several languages with NLP and language models, with personal dashboards and AI-generated summaries in real time.
How classic topic modeling works
The basic assumption is simple: a document about healthy eating will mention fruit and vegetables often, a document about cars words like engine or wheel. Topics are clusters of words that tend to appear together. The whole approach rests on statistics.
The best-known algorithm is latent Dirichlet allocation (LDA). It represents each document as a mix of topics and each topic as a mix of words, using two probabilities: how likely a word is in a topic, and how likely a topic is in a document.
- Prepare the text: remove punctuation, split the text into words, drop very common words such as “and” or “the”, and reduce words to their base form (lemmatisation).
- Build a word representation: each document becomes a “bag of words”, a count of which words occur and how often. Word order is ignored.
- Train the model: LDA finds a given number of topics and lists the most probable words for each.
- Interpret the result: a topic with the words “pasta, pizza, restaurant” is clearly about Italian food, one with “wheel, suspension, drive” about cars.
The number of topics has to be set in advance, even though nobody knows it yet. A common approach is to train several models with different numbers of topics and compare their topic coherence, a measure of how well the words in a topic belong together. Too many topics produce overlapping sub-themes with repeated keywords. Visual tools help to check whether topics are clearly separated.
What has changed: embeddings and language models
LDA remains a solid, transparent method. Since the first version of this article, newer approaches have added to it:
| Classic (LDA) | Embeddings and language models | |
|---|---|---|
| How texts are compared | by shared words | by meaning, even with different wording |
| Short texts | often weak | works well, for example for tickets or posts |
| Topic labels | word lists that people interpret | language models suggest readable names |
| Effort | low computing cost, easy to explain | more computing power, often better results |
Methods such as BERTopic turn texts into embeddings, numerical representations of meaning, group similar texts and then describe each group. A language model can name and summarise the topics so that business teams can use the results directly.
In practice: Re-Work
The conference organiser Re-Work wanted to know what attendees were saying on social media during live events. We built a real-time tool that scores sentiment, identifies the most influential voices and detects emerging topics automatically, without manual review.
Getting started with topic modeling
- Choose the question: for example, what are complaints about, or which policy themes matter to us?
- Collect the texts: gather documents, tickets or posts and clarify data protection for personal data.
- Pick the method: LDA for long documents and explainable results, embedding-based methods for short texts and readable topic names.
- Check with the business team: topics only create value when the people who know the domain recognise them as meaningful.
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Request a process analysisFrequently asked questions
An unsupervised machine learning method that automatically finds the recurring themes in a large collection of texts, without predefined categories.
Latent Dirichlet allocation represents each document as a mix of topics and each topic as a mix of words. It learns these distributions from word frequencies and lists the most probable words for each topic.
By training several models with different numbers of topics and comparing their topic coherence, a measure of how well the words in a topic belong together.
They group texts by meaning instead of shared words, work better on short texts and can give topics readable names and summaries.