Insights on the Use of Sentiment Analysis in the Context of Higher Education

Abstract

The extensive use of digital platforms as part of the new technological revolution in higher education (HE) has triggered a massive generation of educational data. Processing this large amount of data is a complex but necessary task in the search for better learning methodologies. Analysing text data, such as comments, reviews, and survey responses, could be useful for instructors and institutions to obtain student feedback. In this sense, sentiment analysis (SA) has emerged as a powerful tool within the field of Natural Language Processing. This study presents a systematic literature review on the use of SA, particularly in the context of HE. We adopted a PRISMA framework as a guide for our systematic research process. Among the main results obtained are: the identification of the most commonly used data sources in SA research in the context of HE, the purpose for which SA is applied, the most used SA approaches and the main challenges in its application.