Mining Opinions from University Students' Feedback using Text Analytics

Angela Lee, Tong Ming Lim

Abstract


Feedback from university students on their experience while studying in any university allows an institute of higher learning to strategize and improve their strategies in order to enrich students' university experiences. In Malaysia, a yearly student survey is conducted to solicit feedback and this research studies the feedback by using text analytics to analyze issues in the form of key terms that were discussed in the feedback among these students. The outcomes of the analysis in this paper will highlight key topics and related sub-topics in their feedback. Another outcome of the analysis highlights clusters of feedback where themes that are closely interrelated will be put into the same cluster. The unstructured feedback in this research analyzes their arrival to the university, learning activities and living experiences. The methodology used in this research entails review of related works, understanding on the importance of student experience, text analysis that consists of text parsing, filtering, and topics and clustering of themes after texts are pre-processed, and finally analyzing the outcomes produced. This paper concludes by drawing several issues to the attention of the institute.

Keywords


Concept Map; Text Analytics; Text Cluster; Text Topics; University Experience

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