  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.
