A Sentiment Analysis Based Approach for Exploring Student Feedback

Student feedback is commonly used as a reliable source of information to evaluate learning outcomes and teaching quality. This feedback has proven to provide faculty not only with valuable insights into how students are learning, but also with an ideal opportunity to reflect on teaching resources and instructional strategies. However, given the increasing massive amounts of feedback that is available online, collecting and analyzing this data manually is not usually an easy task. The aim of this work is, therefore, to put forward a sentiment analysis classifier that is capable of categorizing student feedback as being either positive or negative. To this end, students’ reviews posted about online courses were automatically extracted, preprocessed and then fed into various machine learning algorithms. The findings of this analysis revealed that the Support Vector Machines (SVM) algorithm achieves the highest accuracy score (93.35%) and, thus, outperforms other implemented models. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.