This research aims to evaluate student engagement in online learning environments using Natural Language Processing (NLP) and sentiment analysis. The research method involves text analysis of student interactions on a Learning Management System (LMS) platform, including discussion forums, comments, and messages. NLP techniques were used to identify patterns of student engagement, while sentiment analysis assessed the emotions contained in the interactions, including positive, negative, or neutral sentiments. The results show that student engagement can be effectively measured through this analysis, as well as providing an overview of engagement patterns and the factors that influence them. The findings are expected to be used to improve the quality of online learning.