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Issue No.10 - Oct. (2013 vol.19)
pp: 1646-1663
Published by the IEEE Computer Society
C. Gorg , Comput. Biosci. Program, Univ. of Colorado, Aurora, CO, USA
Zhicheng Liu , Dept. of Comput. Sci., Stanford Univ., Stanford, CA, USA
Jaeyeon Kihm , Cornell CIS, Ithaca, NY, USA
Jaegul Choo , Coll. of Comput., Georgia Inst. of Technol., Atlanta, GA, USA
Haesun Park , Coll. of Comput., Georgia Inst. of Technol., Atlanta, GA, USA
J. Stasko , Sch. of Interactive Comput., Georgia Inst. of Technol., Atlanta, GA, USA
Investigators across many disciplines and organizations must sift through large collections of text documents to understand and piece together information. Whether they are fighting crime, curing diseases, deciding what car to buy, or researching a new field, inevitably investigators will encounter text documents. Taking a visual analytics approach, we integrate multiple text analysis algorithms with a suite of interactive visualizations to provide a flexible and powerful environment that allows analysts to explore collections of documents while sensemaking. Our particular focus is on the process of integrating automated analyses with interactive visualizations in a smooth and fluid manner. We illustrate this integration through two example scenarios: An academic researcher examining InfoVis and VAST conference papers and a consumer exploring car reviews while pondering a purchase decision. Finally, we provide lessons learned toward the design and implementation of visual analytics systems for document exploration and understanding.

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