Visualized Emotion Ontology: A model for representing visual cues of emotions

Rebecca Lin, Muhammad Amith, Chen Liang, Rui Duan, Yong Chen, Cui Tao

Research output: Contribution to journalArticlepeer-review

Abstract

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.

Original languageEnglish (US)
Article number64
JournalBMC Medical Informatics and Decision Making
Volume18
DOIs
StatePublished - Jul 23 2018
Externally publishedYes

Keywords

  • Crowdsourcing
  • Emotion
  • Graphical user interfaces
  • Human computer interaction
  • Knowledge engineering
  • Knowledge representation
  • Public healthcare
  • Semantic web
  • Software agents

ASJC Scopus subject areas

  • Health Policy
  • Health Informatics
  • Computer Science Applications

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