The DOI matches its Crossref record; 23 of the 23 references it cites were found in a registry or an index.
Termite: visualization techniques for assessing textual topic models
Abstract
Topic models aid analysis of text corpora by identifying latent topics based on co-occurring words. Real-world deployments of topic models, however, often require intensive expert verification and model refinement. In this paper we present Termite, a visual analysis tool for assessing topic model quality. Termite uses a tabular layout to promote comparison of terms both within and across latent topics. We contribute a novel saliency measure for selecting relevant terms and a seriation algorithm that both reveals clustering structure and promotes the legibility of related terms. In a series of examples, we demonstrate how Termite allows analysts to identify coherent and significant themes.
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Cited by
Abdul, A., Vermeulen, J., Wang, D., Lim, B. Y., & Kankanhalli, M. (2018) · Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems · doi:10.1145/3173574.3174156 · Cited by 743
Engelmann, S., Chen, M., Fischer, F., Kao, C.-Y., & Grossklags, J. (2019) · Proceedings of the Conference on Fairness, Accountability, and Transparency · doi:10.1145/3287560.3287585 · Cited by 98
Hoque, M. N., Mashiat, T., Ghai, B., Shelton, C. D., Chevalier, F., Kraus, K., & Elmqvist, N. (2024) · Proceedings of the CHI Conference on Human Factors in Computing Systems · doi:10.1145/3613904.3641895 · Cited by 48
Metadata
- Authors
- Chuang, J., Manning, C. D., & Heer, J.
- Venue
- Proceedings of the International Working Conference on Advanced Visual Interfaces
- Publisher
- ACM
- Published
- 2012-05-21
- Pages
- 74-77
- OpenAlex
- W2090491854
- Cited by
- 420
- References
- 23
- FWCI
- 11.17
- Type
- conference paper
- Language
- en
- Open access
- closed
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