An Evaluation of Semantically Grouped Word Cloud Designs

Marti A. Hearst*, Emily Pedersen, Lekha Patil, Elsie Lee, Paul Laskowski, Steven Franconeri

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

5 Scopus citations

Abstract

Word clouds continue to be a popular tool for summarizing textual information, despite their well-documented deficiencies for analytic tasks. Much of their popularity rests on their playful visual appeal. In this paper, we present the results of a series of controlled experiments that show that layouts in which words are arranged into semantically and visually distinct zones are more effective for understanding the underlying topics than standard word cloud layouts. White space separators and/or spatially grouped color coding led to significantly stronger understanding of the underlying topics compared to a standard Wordle layout, while simultaneously scoring higher on measures of aesthetic appeal. This work is an advance on prior research on semantic layouts for word clouds because that prior work has either not ensured that the different semantic groupings are visually or semantically distinct, or has not performed usability studies. An additional contribution of this work is the development of a dataset for a semantic category identification task that can be used for replication of these results or future evaluations of word cloud designs.

Original languageEnglish (US)
Article number8665933
Pages (from-to)2748-2761
Number of pages14
JournalIEEE Transactions on Visualization and Computer Graphics
Volume26
Issue number9
DOIs
StatePublished - Sep 1 2020

Keywords

  • Information visualization
  • data analytics
  • evaluation
  • text analysis
  • word clouds

ASJC Scopus subject areas

  • Software
  • Signal Processing
  • Computer Vision and Pattern Recognition
  • Computer Graphics and Computer-Aided Design

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