Mining Opinions in User-Generated Contents to Improve Course Evaluation

Abstract

The purpose of this paper is to show how opinion mining may offer an alternative way to improve course evaluation using students’ attitudes posted on Internet forums, discussion groups and/or blogs, which are collectively called user-generated content. We propose a model to mine knowledge from students’ opinions to improve teaching effectiveness in academic institutes. Opinion mining is used to evaluate course quality in two steps: opinion classification and opinion extraction. In opinion classification, machine learning methods have been applied to classify an opinion as positive or negative for each student’s posts. Then, we used opinion extraction to extract features, such as teacher, exams and resources, from the user-generated content for a specific course. Then we grouped and assigned orientations for each feature.