Opinion Target Extraction for Student Course Feedback

Student feedback is an essential part of the instructor student relationship. Traditionally student feedback is manually summarized by instructors, which is time consuming. Automatic student feedback summarization provides a potential solution to this. For summarizing student feedback, first, the opinion targets should be identified and extracted. In this context, opinion targets such as “lecture slides”, “teaching style” are the important key points in the feedback that the students have shown their sentiment towards. In this paper, we focus on the opinion target extraction task of general student feedback. We model this problem as an information extraction task and extract opinion targets using a Conditional Random Fields (CRF) classifier. Our results show that this classifier outperforms the state-of-the-art techniques for student feedback summarization.