A Review of Sentiment Analysis Approaches for Quality Assurance in Teaching and Learning

Abstract The education industry considers quality to be a crucial factor in its development. Yet, the quality of many institutions is far from perfect as there is high rate of systemic failure and low performance among students. Consequently, the application of digital computing plays an increasingly important role in assuring the overall quality of an educational institution. However, literature lacks a reasonable number of systematic review that classifies researches that applied natural language processing and machine learning solutions for students’ feedback in sentiment analysis and quality assurance. Thus, this paper presents a systematic literature review that structure available published papers between 2014 and 2023 in high impact journal indexed databases. The work extracted 59 relevant papers out of the 3392 that was initially found using an exclusion and inclusion criteria. The result identified five (5) prevalent techniques that are majorly researched for sentiment analysis in the area of education as well as the prevalent supervised machine learning algorithms, lexicon-based approaches and evaluation metrics in assessing feedbacks in the education domain.