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

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

doi:10.1186/s12911-018-0634-6

Abstract

BackgroundHealthcare 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.MethodsIn 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.ResultsAs 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.ConclusionFrom 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.

Highlights

Healthcare services, 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
We expound on the detailed design motivations behind the Visualized Emotion Ontology (VEO) and its linked visualizations, and in addition, we provided an evaluation of the ontology using Burton-Jones’ semiotic metric suite and validated the visualizations using a crowdsourcing platform
We compared the VEO to a sample of five cognitive ontologies (Mental State Assessment, Emotion Ontology, Mental Functioning Ontology, the Behavior Change Technique Taxonomy, and the Cognitive Atlas Ontology), which would provide us with a baseline measurement

Summary

Introduction

Healthcare services, 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. Patients who report higher levels of positivity tend to participate more during health care service encounters [8], which is beneficial for all parties involved in a clinical experience, improving both perceived quality of service and customer satisfaction. Emotional contagion has been confirmed by neural mechanisms, because an fMRI study has revealed that observing others’ happiness activates the left anterior cingulate gyrus, while observing others’ sadness activates the right inferior frontal gyrus [10] It does not make sense for physicians to remain positive all the time, as they often need to deliver upsetting diagnoses or prognoses, so communication skills training [11, 12] would be useful in teaching them how to give bad news while minimizing detrimental effects to a patient’s mental state. It is essential that healthcare providers can adequately understand and respond to their patients’ emotions to best ensure their well-being

Methods

Results

Discussion

Conclusion