LDAExplore: Visualizing Topic Models Generated Using Latent Dirichlet Allocation

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1507.06593v1.pdf (760.49 KB)

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http://arxiv.org/abs/1507.06593

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https://doi.org/10.48550/arXiv.1507.06593
 http://hdl.handle.net/11603/37213

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UMBC Computer Science and Electrical Engineering Department
UMBC Faculty Collection
UMBC Information Systems Department
UMBC Student Collection

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https://orcid.org/0000-0002-5989-8543

Date

2015-07-23

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journal articles
preprints
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Subjects

Computer Science - Human-Computer Interaction
Computer Science - Information Retrieval

Abstract

We present LDAExplore, a tool to visualize topic distributions in a given document corpus that are generated using Topic Modeling methods. Latent Dirichlet Allocation (LDA) is one of the basic methods that is predominantly used to generate topics. One of the problems with methods like LDA is that users who apply them may not understand the topics that are generated. Also, users may find it difficult to search correlated topics and correlated documents. LDAExplore, tries to alleviate these problems by visualizing topic and word distributions generated from the document corpus and allowing the user to interact with them. The system is designed for users, who have minimal knowledge of LDA or Topic Modelling methods. To evaluate our design, we run a pilot study which uses the abstracts of 322 Information Visualization papers, where every abstract is considered a document. The topics generated are then explored by users. The results show that users are able to find correlated documents and group them based on topics that are similar.