---
title: "BERTopic: Neural topic modeling with a — Grootendorst 2022"
description: "BERTopic addresses a weakness of classical topic models such as LDA and NMF."
authors: ["Maarten Grootendorst"]
year: 2022
venue: "arXiv:2203.05794"
source: "https://arxiv.org/abs/2203.05794"
doi: "10.48550/arxiv.2203.05794"
arxiv: "2203.05794"
licence: "arXiv — non-exclusive licence to distribute"
licence_url: "http://arxiv.org/licenses/nonexclusive-distrib/1.0/"
summary_licence: "Elamur Cosmic License"
canonical: "https://research.elamur.ai/free/en/papers/grootendorst-2022-bertopic-neural-topic-modeling-class/"
---
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# BERTopic: Neural topic modeling with a class-based TF-IDF procedure

**Authors:** Maarten Grootendorst · **Year:** 2022 · **Venue:** arXiv:2203.05794 · **Licence:** [arXiv — non-exclusive licence to distribute](http://arxiv.org/licenses/nonexclusive-distrib/1.0/)

**Links:** [Publisher's page](https://arxiv.org/abs/2203.05794) · [PDF](https://arxiv.org/pdf/2203.05794) · [DOI 10.48550/arxiv.2203.05794](https://doi.org/10.48550/arxiv.2203.05794) · [arXiv 2203.05794](https://arxiv.org/abs/2203.05794) · [OpenAlex](https://openalex.org/W4221142221)

**Citations:** 1342 citations (openalex-snapshot · 2026-10-14) · [384](#cited-by) in our library, of the 140673 papers whose bibliographies we read

## Summary
BERTopic addresses a weakness of classical topic models such as LDA and NMF. Because they treat documents as bags of words, they ignore semantic context. Earlier embedding-based methods (Sia et al., Top2Vec) improved on this, but they label topics by picking words near a cluster centroid, which misleads when clusters are not spherical. Grootendorst's approach has three steps. Documents are embedded with a pre-trained Sentence-BERT model. UMAP then reduces the dimensionality of these embeddings, and HDBSCAN groups them into density-based clusters, setting noisy documents aside as outliers. Each cluster is finally described with a class-based TF-IDF (c-TF-IDF), which scores how distinctive a word is for the concatenated text of that cluster rather than for a single document. Because the steps are decoupled, the embedding model can be swapped and topic wording tuned without re-clustering. The same design also supports dynamic topic modeling over time or other metadata. Across benchmarks against classical and clustering-based models, BERTopic gives coherent topics with competitive, stable performance. Its quality should rise as language models improve.

## Key points
- **Pipeline:** SBERT embeddings, then UMAP, then HDBSCAN, then c-TF-IDF. The weight is W(t,c) = tf(t,c) · log(1 + A / tf(t)), where A is the mean word count per class. Topics can be reduced to a chosen number by repeatedly merging the rarest topic into its most similar one.
- **Dynamic topics:** the model is first fitted globally. Local topic representations per timestep are then obtained by combining that timestep's term frequencies with the precomputed global IDF values. This avoids re-embedding or re-clustering, and optional smoothing links consecutive timesteps.
- **Wall time on the Trump dataset (1,000–43,000 documents, steps of 2,000):**
  - CTM with MP-NET was by far the slowest.
  - LDA and NMF were the fastest.
  - BERTopic and Top2Vec ran at similar speeds when using the same language model.
  - With a GPU, MiniLM ran about as fast as Doc2Vec.
- **Stated weaknesses:**
  - Each document is assumed to carry a single topic; HDBSCAN probabilities serve only as a partial proxy for mixed topics.
  - Topic words come from bags-of-words and can be redundant; maximal marginal relevance was proposed as a fix but not tested.

*Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.*

Fields: [Computing & GPUs](https://md.research.elamur.ai/free/en/shelves/computing/)

## Cited by

Cited by

384 papers of our library cite it (of the 140673 whose bibliographies we read); 12 of them have a page here, 372 not yet.

- [Content-Localization based System for Analyzing Sentiment and Hate Behaviors in Low-Resource Dialectal Arabic: English to Levantine and Gulf](https://md.research.elamur.ai/free/en/papers/alzamzami-2023-content-localization-based-system-analyzing/)
- [TATA: Stance Detection via Topic-Agnostic and Topic-Aware Embeddings](https://md.research.elamur.ai/free/en/papers/hanley-2023-tata-stance-detection-topic-agnostic/)
- [Evaluating the Impact of Source Diversity for RAG in Historical Research](https://md.research.elamur.ai/free/en/papers/mahadeshwar-2026-evaluating-impact-source-diversity-rag/)
- [A Computational Diachronic Analysis of Gen Z Mental Health Discourse: A Large-scale Reddit Corpus Study from Pre- to Post-COVID](https://md.research.elamur.ai/free/en/papers/mao-2026-computational-diachronic-analysis-gen-z/)
- [What Are LLMs Doing to Scientific Communication? Measuring Changes in Writing Practices and Reading Experience](https://md.research.elamur.ai/free/en/papers/miletic-2026-what-are-llms-doing-scientific/)
- [Uncovering Conspiratorial Narratives within Arabic Online Content](https://md.research.elamur.ai/free/en/papers/mohdeb-2025-uncovering-conspiratorial-narratives-within-arabic/)
- [Bounded Conversational Memory with Hybrid Retrieval and Evidence Highlighting for Multi-Session Dialogue Systems](https://md.research.elamur.ai/free/en/papers/mudunuri-2026-bounded-conversational-memory-hybrid-retrieval/)
- [ACID: On the Perception of Online Classism](https://md.research.elamur.ai/free/en/papers/muti-2026-acid-perception-online-classism/)
- [When Numbers Tell Half the Story: Human-Metric Alignment in Topic Model Evaluation](https://md.research.elamur.ai/free/en/papers/prouteau-2026-when-numbers-tell-half-story/)
- [The Spectrum of Sentiment: Optimistic, Pessimistic, and Neutral Voices in Online Depression Discourse](https://md.research.elamur.ai/free/en/papers/tabusca-2026-spectrum-sentiment-optimistic-pessimistic-neutral/)
- [Audience Engagement with Arabic Women's Social Empowerment and Wellbeing: A Decadal Corpus](https://md.research.elamur.ai/free/en/papers/zaghouani-2026-audience-engagement-arabic-women-s/)
- [From Noise to Signal: When Outliers Seed New Topics](https://md.research.elamur.ai/free/en/papers/zve-2026-noise-signal-when-outliers-seed/)

## Cite

The paper itself, not this page. BibTeX from the library's record of the work.

```bibtex
@misc{grootendorst2022bertopic,
  title = {{BERTopic: Neural topic modeling with a class-based TF-IDF procedure}},
  author = {Grootendorst, Maarten},
  year = {2022},
  doi = {10.48550/arxiv.2203.05794},
  eprint = {2203.05794},
  archivePrefix = {arXiv},
  url = {https://doi.org/10.48550/arxiv.2203.05794}
}
```

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