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Abstract

Word clouds provide an effective way to visually summarize important keywords from a large collection of text. Despite their increasing popularity, relatively less attention has been paid on developing interactive techniques for flexible word cloud navigation and manipulation. In this article, we present a focus + context display technique to support multifaceted viewing of word clouds. In our algorithm, the sizes of words in a word cloud are first changed to reflect the current importance metric selected by the user and then scaled to balance space utilization and word readability. To remove word overlaps caused by changes of sizes while maintaining the positional dependency modeled by a directed acyclic graph, we propose a force-directed model that also maximizes the utilization of display space. We demonstrate the effectiveness and usefulness of our techniques through case studies using a real-world dataset and evaluate the performance of the constraint-based overlap removal method in achieving stable layouts during importance transformation.

Keywords

Text visualization multi-attribute visualization focus+context visualization tag cloud multivariate data

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Supporting multifaceted viewing of word clouds with focus+context display

Abstract

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