An Automated Approach for Analysing Students Feedback Using Sentiment Analysis Techniques

Abstract

Conducting and evaluating continuous student feedback is essential for any quality enhancement cell (QEC) within an education institution. Students’ feedback based on their personal opinions can play a vital role in ensuring quality education. However, students’ subjective opinions are often ignored due to time constraints or a lack of adequate analysis strategies. Therefore, to automate the quality assurance process, two classification models (i.e., based on Monkey learn API and SentiWord using TextBlob) are proposed to analyze students’ feedback data. The results shows that the model employing MonkeyLearn performs nearly 22% points better than the Textblob on the Albanian language dataset obtained from 114 students’ responses, achieving 72.12% accuracy.