Topic Modeling — Latent Dirichlet Allocation
Also known as: LDA, latent Dirichlet allocation, Konu Modelleme — LDA
Latent Dirichlet Allocation (LDA) is a generative probabilistic model introduced by Blei, Ng and Jordan (2003) that extracts the hidden topic distributions underlying a collection of documents. It treats each document as a mixture of latent topics and each topic as a distribution over words, turning an unlabelled corpus into interpretable themes.
Key highlights
- Discovers latent themes in an unlabelled corpus without any manual tagging.
- Represents each document as a mixture of topics, capturing that texts often span several themes at once.
- Produces interpretable output: topics described by their most characteristic words.
Intuition
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How it works
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When to use it
Use LDA when you have a corpus of text documents and want to discover the latent themes running through it without pre-labelled categories. It is suited to exploratory and descriptive work on unstructured text. The corpus must be large enough — at least around 50 documents, and below roughly 100 the topics become unstable — and the number of topics k has to be chosen, ideally via a coherence score. With no text data, topic modeling cannot run.
Strengths & limitations
- Discovers latent themes in an unlabelled corpus without any manual tagging.
- Represents each document as a mixture of topics, capturing that texts often span several themes at once.
- Produces interpretable output: topics described by their most characteristic words.
- Requires a sufficiently large corpus; on too few documents (under roughly 100) the topics become unstable.
- The number of topics k must be specified in advance and chosen carefully, e.g. via a coherence score.
- The bag-of-words assumption discards word order and context.
Common pitfalls
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Applications
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Frequently asked
How do I choose the number of topics k?
k is set before fitting and should be chosen deliberately rather than guessed. The common approach is to fit the model for several candidate values of k and compare coherence scores, then keep the value that produces the most interpretable, well-separated topics.
How many documents does LDA need?
A minimum of around 50 documents is expected, but below roughly 100 the topics tend to become unstable and inconsistent. On very small corpora a simple frequency analysis is the safer choice.
What does LDA actually output?
For every document it estimates a distribution over the k topics, and for every topic a distribution over words. You read each topic through its highest-weighted words to label it, and describe each document by its mixture of topics.
Can I use LDA without text data?
No. LDA operates on a corpus of text documents represented as word counts. With no text data, topic modeling cannot be applied and a qualitative thematic analysis should be used instead.
Sources
- 1.Blei, D.M., Ng, A.Y. & Jordan, M.I. (2003). Latent Dirichlet Allocation. Journal of Machine Learning Research, 3, 993-1022.
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Cite this page
ScholarGate. (2026, June 1). Topic Modeling (LDA). ScholarGate. https://scholargate.app/text-mining/topic-modeling-lda