Journal
PROCEEDINGS OF THE INTERNATIONAL WORKING CONFERENCE ON ADVANCED VISUAL INTERFACES
Volume -, Issue -, Pages 74-77Publisher
ASSOC COMPUTING MACHINERY
DOI: 10.1145/2254556.2254572
Keywords
Topic Models; Text Visualization; Seriation
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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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