Process / pipeline

BERTopic — Neural Topic Modeling

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.

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Sources

  1. Grootendorst, M. (2022). BERTopic: Neural topic modeling with a class-based TF-IDF procedure. arXiv:2203.05794. DOI: 10.48550/arXiv.2203.05794
  2. McInnes, L., Healy, J. & Astels, S. (2017). hdbscan: Hierarchical density based clustering. Journal of Open Source Software, 2(11), 205. DOI: 10.21105/joss.00205

Related methods

Referenced by

ScholarGateBERTopic (BERTopic — Neural Topic Modeling). Retrieved 2026-06-04 from https://scholargate.app/en/text-mining/topic-modeling-bertopic