Behavioral Analysis for User Satisfaction

Today's Web aims to increase engagement with the user, his or her emotions and behaviors. Contemplating the emotional sphere is potentially very useful when it comes to Web sites: understanding how the user feels can help the service provider understand his or her needs and improve the service he or she offers. This project aims to devise a behavioral feature extraction system to create an interactive system that can provide a personalized experience and maximize user satisfaction. The system contemplates behavioral analysis techniques using Machine Learning, Recommender System and Emotion Recognition algorithms. In the experimental phase, users tested the system through Expressing Mixed Emotions, SUS and SUPR-Q tasks and questionnaires with the aim of evaluating the User Experience and The Usability of the system.