Computer Science > Computation and Language
arXiv:2212.08459 (cs)
[Submitted on 16 Dec 2022]
Title:Experiments on Generalizability of BERTopic on Multi-Domain Short Text
View a PDF of the paper titled Experiments on Generalizability of BERTopic on Multi-Domain Short Text, by Muri\"el de Groot and 2 other authors
View PDFAbstract: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.
| Comments: | Accepted poster presentation at WiNLP 2022, as a part of EMNLP 2022, 2 pages |
| Subjects: | Computation and Language (cs.CL) |
| Cite as: | arXiv:2212.08459 [cs.CL] |
| (orarXiv:2212.08459v1 [cs.CL] for this version) | |
| https://doi.org/10.48550/arXiv.2212.08459 arXiv-issued DOI via DataCite |
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View a PDF of the paper titled Experiments on Generalizability of BERTopic on Multi-Domain Short Text, by Muri\"el de Groot and 2 other authors
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