StoryGem: Voronoi treemap Approach for Semantics-Preserving Text Visualization
Naoya Oda, Yosuke Onoue

TL;DR
StoryGem introduces a Voronoi treemap-based text visualization that preserves semantic relationships, improves space utilization, and accurately represents word frequencies by mapping them to area sizes instead of font sizes.
Contribution
It presents a novel visualization method combining semantic clustering with Voronoi treemaps, optimizing space use and frequency representation in text visualization.
Findings
User study shows improved semantic understanding.
Effective space utilization in visualizations.
Clearer frequency representation through area sizing.
Abstract
Word cloud use is a popular text visualization technique that scales font sizes based on word frequencies within a defined spatial layout. However, traditional word clouds disregard semantic relationships between words, arranging them without considering their meanings. Semantic word clouds improved on this by positioning related words in proximity; however, still struggled with efficient space use and representing frequencies through font size variations, which can be misleading because of word length differences. This paper proposes StoryGem, a novel text visualization approach that addresses these limitations. StoryGem constructs a semantic word network from input text data, performs hierarchical clustering, and displays the results in a Voronoi treemap. Furthermore, this paper proposes an optimization problem to maximize the font size within the regions of a Voronoi treemap. In…
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