# Experiments on Generalizability of BERTopic on Multi-Domain Short Text

arXiv: https://arxiv.org/abs/2212.08459 · HTML: https://mlsanity.com/paper/2212.08459/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 11 references that are in the map.
- First posted on arXiv 16 Dec 2022.

## Builds on (most cited first, 4 of 4)

- [BERTopic: Neural topic modeling with a class-based TF-IDF procedure](https://mlsanity.com/paper/2203.05794/) (2022)
- [Topic Modeling in Embedding Spaces](https://mlsanity.com/paper/1907.04907/) (2019)
- [Topic Modeling over Short Texts by Incorporating Word Embeddings](https://mlsanity.com/paper/1609.08496/) (2016)
- [Word Network Topic Model: A Simple but General Solution for Short and Imbalanced Texts](https://mlsanity.com/paper/1412.5404/) (2014)

## Built on by (most cited first, 9 of 9)

- [More than Memes: A Multimodal Topic Modeling Approach to Conspiracy Theories on Telegram](https://mlsanity.com/paper/2410.08642/) (2024)
- [Multilingual transformer and BERTopic for short text topic modeling: The case of Serbian](https://mlsanity.com/paper/2402.03067/) (2024)
- [Discovering Significant Topics from Legal Decisions with Selective Inference](https://mlsanity.com/paper/2401.01068/) (2024)
- [LLM-Assisted Topic Reduction for BERTopic on Social Media Data](https://mlsanity.com/paper/2509.19365/) (2025)
- [The correlation between nativelike selection and prototypicality: a multilingual onomasiological case study using semantic embedding](https://mlsanity.com/paper/2405.13529/) (2024)
- [Semantic Component Analysis: Introducing Multi-Topic Distributions to Clustering-Based Topic Modeling](https://mlsanity.com/paper/2410.21054/) (2024)
- [Unravelling Technical debt topics through Time, Programming Languages and Repository](https://mlsanity.com/paper/2504.11714/) (2025)
- [BERTopic-Virality Prioritisation: A Scalable Framework for Thematic and Comparative Analysis of COVID-19 and Monkeypox Misinformation on Twitter](https://mlsanity.com/paper/2608.15691/) (2026)
- [Pre-retrieval Query Clustering for Adaptive Top-k Document Retrieval in RAG Systems](https://mlsanity.com/paper/2609.13489/) (2026)

## Abstract (arXiv)

Topic modeling is widely used for analytically evaluating large collections of textual data. One of the most popular topic techniques is Latent Dirichlet Allocation (LDA), which is flexible and adaptive, but not optimal for e.g. short texts from various domains. We explore how the state-of-the-art BERTopic algorithm performs on short multi-domain text and find that it generalizes better than LDA in terms of topic coherence and diversity. We further analyze the performance of the HDBSCAN clustering algorithm utilized by BERTopic and find that it classifies a majority of the documents as outliers. This crucial, yet overseen problem excludes too many documents from further analysis. When we replace HDBSCAN with k-Means, we achieve similar performance, but without outliers.