Abstract: 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. Firstly, This approach extracts topics from textual corpora by using 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 relationships 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 in a variety of data sets including IEEE Transactions 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.

Key words: visual analysis, focus+context, level of detail, topic modeling, user interactions