Semantic-Preserving Word Clouds by Seam Carving

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 collection of documents 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.