Using sentiment analysis to evaluate qualitative students� responses

Abstract

Text analytics in education has evolved to form a critical component of the future SMART campus architecture. Sentiment analysis and qualitative feedback from students is now a crucial application domain of text analytics relevant to institutions. The implementation of sentiment analysis helps understand learners� appreciation of lessons, which they prefer to express in long texts with little or no restriction. Such expressions depict the learner�s emotions and mood during class engagements. This research deployed four classifiers, including Na�ve Bayes (NB), Support Vector Machine (SVM), J48 Decision Tree (DT), and Random Forest (RF), on a qualitative feedback text after a semester-based course session at the University of Education, Winneba. After enough training and testing using the k-fold cross-validation technique, the SVM classification algorithm performed with a superior accuracy of 63.79%. � 2022, The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.