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

LDAExplore: Visualizing Topic Models Generated Using Latent Dirichlet Allocation