Unveiling Sentiments: Analyzing Learner's Experience Using VADER and RoBERTa Models

Educational Institutions receive a large amount of data, which includes student review. The text-based interactions, reveal patterns in student sentiments and emotions and enhance the overall educational experience. The text analytics allows institutions to perform analysis on these feedbacks, that gives insights to the institution on student satisfaction, identifying areas of improvement, and analyzing the students' response. The Sentiment analysis is performed on reviews to help institutions understand the student needs and their engagement in courses in a MOOC environment. The sentiment analysis models are used to classify positive or negative reviews or to determine emotions such as happy or sad. In this research work two approaches, lexicon and transformer based algorithms were used to understand the review of Coursera data, The analysis further shows sentiment of learners based on different domains of study.