Visualized Emotion Ontology: a model for representing visual cues of emotions.
BACKGROUND
Healthcare services, particularly in patient-provider interaction, often involve highly emotional situations, and it is important for physicians to understand and respond to their patients' emotions to best ensure their well-being.


METHODS
In order to model the emotion domain, we have created the Visualized Emotion Ontology (VEO) to provide a semantic definition of 25 emotions based on established models, as well as visual representations of emotions utilizing shapes, lines, and colors.


RESULTS
As determined by ontology evaluation metrics, VEO exhibited better machine-readability (z=1.12), linguistic quality (z=0.61), and domain coverage (z=0.39) compared to a sample of cognitive ontologies. Additionally, a survey of 1082 participants through Amazon Mechanical Turk revealed that a significantly higher proportion of people agree than disagree with 17 out of our 25 emotion images, validating the majority of our visualizations.


CONCLUSION
From the development, evaluation, and serialization of the VEO, we have defined a set of 25 emotions using OWL that linked surveyed visualizations to each emotion. In the future, we plan to use the VEO in patient-facing software tools, such as embodied conversational agents, to enhance interactions between patients and providers in a clinical environment.
['Lin|Rebecca|R|', 'Amith|Muhammad Tuan|MT|', 'Liang|Chen|C|', 'Duan|Rui|R|', 'Chen|Yong|Y|', 'Tao|Cui|C|']
D001185:Artificial Intelligence D063045:Crowdsourcing D003463:Cues D003695:Delivery of Health Care D004644:Emotions D005260:Female D006801:Humans D008297:Male D012660:Semantics D012984:Software D014584:User-Computer Interface
Emotion Crowdsourcing Graphical user interfaces Human computer interaction Knowledge engineering Knowledge representation Public healthcare Semantic web Software agents