Visualizing Topic Uncertainty in Topic Modelling Article Swipe
Word clouds became a standard tool for presenting results of natural language processing methods such as topic modelling. They exhibit most important words, where word size is often chosen proportional to the relevance of words within a topic. In the latent Dirichlet allocation (LDA) model, word clouds are graphical presentations of a vector of weights for words within a topic. These vectors are the result of a statistical procedure based on a specific corpus. Therefore, they are subject to uncertainty coming from different sources as sample selection, random components in the optimization algorithm, or parameter settings. A novel approach for presenting word clouds including information on such types of uncertainty is introduced and illustrated with an application of the LDA model to conference abstracts.
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Metadata
- Type
- preprint
- Language
- en
- Landing Page
- http://arxiv.org/abs/2302.06482
- https://arxiv.org/pdf/2302.06482
- OA Status
- green
- Cited By
- 2
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4320854923
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
-
https://openalex.org/W4320854923Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.48550/arxiv.2302.06482Digital Object Identifier
- Title
-
Visualizing Topic Uncertainty in Topic ModellingWork title
- Type
-
preprintOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2023Year of publication
- Publication date
-
2023-02-13Full publication date if available
- Authors
-
Peter WinkerList of authors in order
- Landing page
-
https://arxiv.org/abs/2302.06482Publisher landing page
- PDF URL
-
https://arxiv.org/pdf/2302.06482Direct link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
-
https://arxiv.org/pdf/2302.06482Direct OA link when available
- Concepts
-
Latent Dirichlet allocation, Topic model, Computer science, Word (group theory), Natural language processing, Artificial intelligence, Relevance (law), Selection (genetic algorithm), Information retrieval, Mathematics, Political science, Geometry, LawTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
2Total citation count in OpenAlex
- Citations by year (recent)
-
2023: 2Per-year citation counts (last 5 years)
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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