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Jeon Cheol Su

dblp:349/4461 · DBLP profile ↗
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1ranked-venue papers
0as first author
1since 2021 · last 2024
0000-0003-2764-5522ORCID · reported

Domains — the database's venue-derived domains; a paper can count in several

Graphics, computer vision, multimedia, augmented reality and games · 1 · 1 since 2021

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Computer graphics and multimedia
1 paper
Visualization and visual analytics · 100%
Human-computer interaction and pervasive computing
1 paper
Usability and user experience research · 100%

Topics — the 2 heaviest of 3, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Visualization and visual analytics › information visualization
affective visualization
0.812024
Creating Emordle: Animating Word Cloud for Emotion Expression · IEEE Trans. Vis. Comput. Graph. 2024
Usability and user experience research › evaluation methodology
crowdsourced evaluation
0.212024
Creating Emordle: Animating Word Cloud for Emotion Expression · IEEE Trans. Vis. Comput. Graph. 2024

Methods — techniques the papers use, named apart from their topics

crowdsourcing study · 1.5
YearPublicationVenuePosition
2024 Creating Emordle: Animating Word Cloud for Emotion Expression
abstract
We propose emordle, a conceptual design that animates wordles (compact word clouds) to deliver their emotional context to audiences. To inform the design, we first reviewed online examples of animated texts and animated wordles, and summarized strategies for injecting emotion into the animations. We introduced a composite approach that extends an existing animation scheme for one word to multiple words in a wordle with two global factors: the randomness of text animation (entropy) and the animation speed (speed). To create an emordle, general users can choose one predefined animated scheme that matches the intended emotion class and fine-tune the emotion intensity with the two parameters. We designed proof-of-concept emordle examples for four basic emotion classes, namely happiness, sadness, anger, and fear. We conducted two controlled crowdsourcing studies to evaluate our approach. The first study confirmed that people generally agreed on the conveyed emotions from well-crafted animations, and the second one demonstrated that our identified factors helped fine-tune the extent of the emotion delivered. We also invited general users to create their own emordles based on our proposed framework. Through this user study, we confirmed the effectiveness of the approach. We concluded with implications for future research opportunities of supporting emotion expression in visualizations.
Liwenhan Xie, Xinhuan Shu, Jeon Cheol Su, Yun Wang 0012, Siming Chen 0001, Huamin Qu
IEEE Trans. Vis. Comput. Graph.3