Actionable emotion detection in context-aware systems

Abstract

Ensuring the quality of user experience is very important for increasing the acceptance likelihood of software applications, which can be affected by several contextual factors that can continuously change over time (e.g., emotional status of end-user). Due to these changes in the context, software continually needs to be (self-) adaptive for delivering software services that can satisfy user needs continuously. So far, online explicit user feedback has become one of the most used information sources for evaluating users’ satisfaction and discovering new requirements of a given software application. However, most of these online reviews are not authenticated, and they may not always be reliable. In order to complement this explicit feedback derived from user reviews, this research proposes an approach that exploits both physiological and contextual data to be used as main inputs for detecting actionable emotions. These actionable emotions, detected during the user interaction with context-aware software applications, can be used as implicit feedback for improving the adaptability of the software and quality of the user experience. The evaluation involved in total 23 subjects in three rounds of experiments. The results of this research support the idea that emotional data expressed by users when interacting with service-based applications can be used as implicit feedback.