An Evaluation of Semantically Grouped Word Cloud Designs

• Published in: IEEE transactions on visualization and computer graphics Vol. 26; no. 9; pp. 2748 - 2761
• Main Authors: Hearst, Marti A., Pedersen, Emily, Patil, Lekha, Lee, Elsie, Laskowski, Paul, Franconeri, Steven
• Format: Journal Article
• Language: English
• Published: United States IEEE 01-09-2020; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Color; Color coding; data analytics; Evaluation; Graphical representations; Information visualization; Layout; Layouts; Semantics; Separators; Tag clouds; Task analysis; text analysis; White spaces; word clouds; Words (language)
• ISSN: 1077-2626; 1941-0506
• DOI: 10.1109/TVCG.2019.2904683

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.