The use of new technologies and the number of users of university online learning systems have spread around the world in the last decades, showing a further increase with the spread of the Covid-19 pandemic since 2020. Additionally, ISO 9241-210:2019 sets international quality standards for designing human-computer interaction products, services, and systems that meet usability, accessibility, and User eXperience (UX) requirements. Therefore, the concept of UX has become very important as a quality requirement. For several authors, UX is a multidimensional concept that includes the motivations, feelings, and needs of end users. On the other hand, the United Nations' (UN) Sustainable Development Goal (SDG) 4 for 2030 aims to ensure inclusive, equitable, quality education for all globally. In this sense, in order to design interfaces and learning experiences in university environments that respect all quality specifications, it is necessary to evaluate the user experience of these environments automatically and accurately beforehand. Thus, the main objective of this thesis is to identify the most relevant specific characteristics in the user experience of university elearning environments that allow specific and automatic analysis of the students' feelings in order to lay the foundations for the design of user-centered e-learning platforms. To this end, the study proposes to analyse the needs and feelings of online university students with digital, advanced, and efficient artificial intelligence methods. Therefore, this project investigates the application of machine learning models of sentiment analysis for the evaluation of user experience. These artificial intelligence techniques have been applied to the responses received from more than 2,000 university students surveyed from postgraduate online studies and massive open online courses (MOOCs). The results present the basis of a model that allows ontologically classifying categories or aspects of university online education and knowing the users' polarity of feeling about their e-learning experience in an automatic way. In this way, it has been possible