Please use this identifier to cite or link to this item: http://hdl.handle.net/1783.1/83783

VISTopic: A Visual Analytics System for Making Sense of Large Document Collections using Hierarchical Topic Modeling

Authors Yang, YI HKUST affiliated (currently or previously).
Yao, Quanming HKUST affiliated (currently or previously)
Qu, Huamin View this author's profile
Issue Date 2017
Source Visual Informatics , v. 1, (1), March 2017, p. 40-47
Summary Effective analysis of large text collections remains a challenging problem given the growing volume of available text data. Recently, text mining techniques have been rapidly developed for automatically extracting key information from massive text data. Topic modeling, as one of the novel techniques that extracts a thematic structure from documents, is widely used to generate text summarization and foster an overall understanding of the corpus content. Although powerful, this technique may not be directly applicable for general analytics scenarios since the topics and topic–document relationship are often presented probabilistically in models. Moreover, information that plays an important role in knowledge discovery, for example, times and authors, is hardly reflected in topic modeling for comprehensive analysis. In this paper, we address this issue by presenting a visual analytics system, VISTopic, to help users make sense of large document collections based on topic modeling. VISTopic first extracts a set of hierarchical topics using a novel hierarchical latent tree model (HLTM) (Liu et al., 2014). In specific, a topic view accounting for the model features is designed for overall understanding and interactive exploration of the topic organization. To leverage multi-perspective information for visual analytics, VISTopic further provides an evolution view to reveal the trend of topics and a document view to show details of topical documents. Three case studies based on the dataset of IEEE VIS conference demonstrate the effectiveness of our system in gaining insights from large document collections.
ISSN 2468-502X
Language English
Format Article
Access View full-text via DOI
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