BERTopic — Neural Topic Modeling
Also known as: neural topic modeling, transformer topic modeling, Konu Modelleme — BERTopic
BERTopic is a neural topic-modeling pipeline introduced by Maarten Grootendorst in 2022. It combines BERT-based contextual embeddings with UMAP dimensionality reduction and HDBSCAN clustering to produce coherent, dynamic topics, achieving higher topic coherence than classic topic models.
Key highlights
- Builds on contextual embeddings, so it captures meaning that bag-of-words models miss.
- Produces coherent, interpretable topics with higher coherence than classic topic models.
- Supports dynamic topic modeling, tracking how topics shift across time on time-stamped corpora.
- A multilingual embedding model lets the same pipeline work across languages.
Intuition
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How it works
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When to use it
Use BERTopic when you have a text corpus and want to discover its underlying themes without specifying them in advance. It suits exploratory and descriptive work on cross-sectional or longitudinal text. A sufficient number of documents is needed — at least 50, with more than 100 recommended — and GPU or high compute is preferred because the embedding step is expensive. With very few documents the density-based clustering has too little signal to form stable topics.
Strengths & limitations
- Builds on contextual embeddings, so it captures meaning that bag-of-words models miss.
- Produces coherent, interpretable topics with higher coherence than classic topic models.
- Supports dynamic topic modeling, tracking how topics shift across time on time-stamped corpora.
- A multilingual embedding model lets the same pipeline work across languages.
- Needs a sufficient number of documents — at least 50, with more than 100 recommended — to form stable topics.
- GPU or high compute is preferred because the transformer embedding step is expensive.
- Results depend on the embedding model and on the UMAP and HDBSCAN settings chosen.
- HDBSCAN leaves some documents unassigned as outliers, so not every document receives a topic.
Common pitfalls
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Applications
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Frequently asked
How is BERTopic different from classic topic models like LDA?
Classic models such as LDA work from word counts and a bag-of-words view of each document. BERTopic starts from contextual transformer embeddings, reduces them with UMAP, clusters them with HDBSCAN, and labels each cluster with class-based TF-IDF terms. Because it captures meaning in context, it generally yields more coherent, interpretable topics.
How many documents do I need?
At least 50 documents, with more than 100 recommended. The clustering step is density-based, so with too few documents there is not enough signal to form stable topics.
Why are some of my documents not assigned to any topic?
HDBSCAN is a density-based clustering algorithm and deliberately leaves genuinely sparse or atypical documents unassigned as outliers rather than forcing them into a topic. This is expected behaviour, not an error.
Can BERTopic handle non-English text?
Yes. By selecting a multilingual embedding model the same pipeline can process corpora in many languages; matching the embedding model to the corpus language is key to good topic quality.
Sources
- 1.Grootendorst, M. (2022). BERTopic: Neural topic modeling with a class-based TF-IDF procedure. arXiv:2203.05794.
- 2.McInnes, L., Healy, J. & Astels, S. (2017). hdbscan: Hierarchical density based clustering. Journal of Open Source Software, 2(11), 205.
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Cite this page
ScholarGate. (2026, June 1). BERTopic. ScholarGate. https://scholargate.app/text-mining/topic-modeling-bertopic