Topic Quester:An Interactive Visual Exploration Of Topic Information From Multiple Sources

This paper presents a visual analysis approach to develop a full picture of relevant topics discussed in multiple textual sources.The key idea behind our approach is to jointly cluster the topics extracted from each source in order to interactively and effectively analyze common and distinctive topics.This approach starts by extracting topics from textual corpora by a correlated topic model method.Different sources of textual corpora are matched together with some common topics.Next,this paper develops a visualization tool consisting of three visualization views for better understanding and analyzing matched topics,as well as the relatively independent ones.The major feature of these three visualizations is that they clearly present what each topic is and the relations among them from multiple perspectives,which allows users to analyze deeper relation-ships and features,from multiple textual corpora and over different periods of time.To meet different users' needs and help better understanding the structure of different textual corpora,these visualizations provide interactive analysis methods at multiple scales.To verify the usability of this method,this paper has applied it to a variety of data sets including IEEE Trans-actions on Knowledge and Data Engineering(TKDE),ACM Transactions on Graphics(TOG),and IEEE Transactions on Visualization and Computer Graphics(TVCG).Qualitative evaluation and several real-world case studies with domain experts demonstrate the promise of our approach,especially in support of analyzing a visualization based full picture of topics at different levels of detail.