Topic discovery and text clustering
Find themes in large text collections and check that the topics you found are real and useful
A taste of a lesson
One of my biggest clusters is labelled 'General feedback'. What should I do with it?
A big vague cluster is a signal, not an answer. Read 20 documents from it. Often you will find texts that are very short, mostly boilerplate such as 'thanks, no comment', or that share a template or signature, which pulls them together regardless of meaning. Fixes: strip boilerplate before embedding, set very short texts aside, or recluster just this group at a finer level. If it is genuinely mixed, report it honestly as unclassified. Quick check: after reading your sample, what share of those 20 documents carry an actual opinion?
Written by the teacher as an example. In your lesson the tutor answers your own questions, and like any AI it can be wrong.
What you will be able to do
- Explain how LDA and embedding based clustering find topics
- Build an embed, reduce and cluster pipeline and describe each cluster
- Choose a topic granularity that fits the decision at hand
- Check cluster labels and stability before reporting results
- Spot clusters driven by language, templates or formatting
Lesson plan
- 1 What topic discovery can and cannot tell you Set realistic expectations for unsupervised theme finding. Start
- 2 LDA intuition Understand topics as word distributions and documents as topic mixtures. Start
- 3 Embed, reduce, cluster Build the modern embedding based pipeline and describe its clusters. Start
- 4 Choosing granularity Compare different numbers of topics and choose one that fits the use. Start
- 5 Naming and checking clusters Label clusters with model help and verify the labels by reading documents. Start
- 6 Traps and next steps Avoid misleading topics and decide how to track themes over time. Start
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About this tutor
For analysts, researchers and builders facing thousands of comments, tickets or articles and wanting to know what they are about. You will learn the intuition behind classic topic models such as LDA, then build the modern pipeline of sentence embeddings, dimensionality reduction and clustering. The lessons cover choosing the number of topics, naming clusters with help from a language model and checking those names, testing whether topics stay stable across runs, and judging usefulness with the people who will act on the results. You will also learn the traps: clusters that reflect formatting or language rather than meaning, and charts that make arbitrary choices look like discoveries.
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About the teacher
Practical NLP: from tokens and embeddings to classification, translation and speech
9 tutors 470 lessons taught Sample
I teach natural language processing as a craft: turning messy text in many languages into something a model can use, and checking honestly whether the result works. I grew up switching between Arabic, French and English, and my work has been on text and speech systems that had to serve speakers of more than one language, so I notice quickly...
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