Semantic-preservingword clouds by seam carving

  • Authors:

  • Yingcai Wu;Thomas Provan;Furu Wei;Shixia Liu;Kwan-Liu Ma

  • Affiliations:

  • The Visualization and Interface Design Innovation Research Group, University of California, Davis;The Visualization and Interface Design Innovation Research Group, University of California, Davis;Mircrosoft Research Asia, Beijing, China;Mircrosoft Research Asia, Beijing, China;The Visualization and Interface Design Innovation Research Group, University of California, Davis

  • Venue:
  • EuroVis'11 Proceedings of the 13th Eurographics / IEEE - VGTC conference on Visualization
  • Year:
  • 2011

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Abstract

Word clouds are proliferating on the Internet and have received much attention in visual analytics. Although word clouds can help users understand the major content of a document collection quickly, their ability to visually compare documents is limited. This paper introduces a new method to create semantic-preserving word clouds by leveraging tailored seam carving, a well-established content-aware image resizing operator. The method can optimize a word cloud layout by removing a left-to-right or top-to-bottom seam iteratively and gracefully from the layout. Each seam is a connected path of low energy regions determined by a Gaussian-based energy function. With seam carving, we can pack the word cloud compactly and effectively, while preserving its overall semantic structure. Furthermore, we design a set of interactive visualization techniques for the created word clouds to facilitate visual text analysis and comparison. Case studies are conducted to demonstrate the effectiveness and usefulness of our techniques.