Detecting Emotions Through Machine Learning for Automatic UX Evaluation
Giuseppe Desolda, Andrea Esposito, Rosa Lanzilotti, Maria F. CostabileAbstract
Although User eXperience (UX) is widely acknowledged as an important aspect of software products, its evaluation is often neglected during the development of most software products, primarily because developers think that it is resource-demanding and complain about the fact that is scarcely automated. Various attempts have been made to develop tools that support and automate the execution of tests with users. This paper is about an ongoing research work that exploits Machine Learning (ML) for automatic UX evaluation, specifically for understanding users’ emotions by analyzing the log data of the users’ interactions with websites. The approach described aims at overcoming some limitations of existing proposals based on ML.