The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.
LDAvis: A method for visualizing and interpreting topics
Open accessAbstract
We present LDAvis, a web-based interac-tive visualization of topics estimated using Latent Dirichlet Allocation that is built us-ing a combination of R and D3. Our visu-alization provides a global view of the top-ics (and how they differ from each other), while at the same time allowing for a deep inspection of the terms most highly asso-ciated with each individual topic. First, we propose a novel method for choosing which terms to present to a user to aid in the task of topic interpretation, in which we define the relevance of a term to a topic. Second, we present results from a user study that suggest that ranking terms purely by their probability under a topic is suboptimal for topic interpretation. Last, we describe LDAvis, our visualization system that allows users to flexibly explore topic-term relationships using relevance to better understand a fitted LDA model. 1
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Griffiths, T. L., & Steyvers, M. (2004) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0307752101 · Cited by 6,049
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“(2015) source code implementation of LDAvis (Sievert and Shirley, 2014).”
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Metadata
- Authors
- Sievert, C., & Shirley, K.
- Venue
- Proceedings of the Workshop on Interactive Language Learning, Visualization, and Interfaces
- Published
- 2014
- Pages
- 63-70
- OpenAlex
- W2160170318
- Cited by
- 1,366
- References
- 1
- FWCI
- 14.24
- Type
- conference paper
- Language
- en
- Open access
- gold
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
