User Experience Evaluation using Machine Learning and Facial Expressions: A Systematic Review - Santos, Bianca Lima; Digiampietri, Luciano A. (Anais do Encontro Nacional de Inteligência Artificial e Computacional (ENIAC); 2024: Anais do XXI Encontro Nacional de Inteligência Artificial e Computacional, 2024)
This systematic review investigates the application of machine learning in facial expressions and emotion recognition within the realm of user experience (UX). The main objective is to identify advances in the state of the art regarding using facial expressions to detect emotions and, consequently, predict or improve user experience. The methodology provided a comprehensive analysis of existing literature, highlighting diverse definitions of UX and their implications for assessing user interactions with systems. Despite efforts to evaluate and enhance UX through various methodologies, few studies focus on predicting UX by integrating emotional states before interaction and user-reported experiences. This gap stems from the absence of a unified UX definition, complicating methodological standardization and result comparability across studies. Many reviewed works emphasize developing recommendation algorithms tailored to music, news, and other content to optimize UX through emotional data. The review identified the challenge of establishing a consistent framework for UX definition across research, revealing varied approaches using different datasets aimed at enhancing recommendations, improving user satisfaction, comparing perceived attitudes, and integrating with established questionnaires.