Towards Emotion-Sensitive Conversational User Interfaces in Healthcare Applications.

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ID: 51373
2019
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Ranked #26 of 170 articles by views in Studies in health technology and informatics

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Abstract
Perception of emotions and adequate responses are key factors of a successful conversational agent. However, determining emotions in a healthcare setting depends on multiple factors such as context and medical condition. Given the increase of interest in conversational agents integrated in mobile health applications, our objective in this work is to introduce a concept for analyzing emotions and sentiments expressed by a person in a mobile health application with a conversational user interface. The approach bases upon bot technology (Synthetic intelligence markup language) and deep learning for emotion analysis. More specifically, expressions referring to sentiments or emotions are classified along seven categories and three stages of strengths using treebank annotation and recursive neural networks. The classification result is used by the chatbot for selecting an appropriate response. In this way, the concerns of a user can be better addressed. We describe three use cases where the approach could be integrated to make the chatbot emotion-sensitive.
Reference Key
denecke2019towardsstudies Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Denecke, Kerstin;May, Richard;Deng, Yihan;
Journal Studies in health technology and informatics
Year 2019
DOI
10.3233/SHTI190409
URL
Keywords
Deep learning sentiment analysis natural language processing conversational user interface

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