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.

本研究探讨了在短的多领域文本中如何运用 BERTopic 算法进行主题建模，并发现与 Latent Dirichlet Allocation 相比，BERTopic 在主题连贯性和多样性方面的表现更好。我们进一步分析了 BERTopic 所使用的 HDBSCAN 聚类算法的性能，并发现其将大多数文档分类为异常值。在使用 k-Means 替换 HDBSCAN 后，我们取得了类似的性能表现，但不再出现异常值。