Mining Opinions from University Students' Feedback using Text Analytics

Angela Lee, Tong Ming Lim


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.


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


M. Shrimoyee, "Theoretical framework for analyzing International students' concept of expectation and consumer satisfaction in an International University," E-Leader Bangkok, 2014.

S. P. Leeman-Munk, E. N. Wiebe, and J. C. Lester, "Assessing elementary students' science competency with text analytics," in Proceedings of the Fourth International Conference on Learning Analytics And Knowledge, ACM, Mar 2014, pp. 143-147.

R. Ferguson and S. B. Shum, "Learning analytics to identify exploratory dialogue within synchronous text chat," in Proceedings of the 1st International Conference on Learning Analytics and Knowledge, ACM, Feb 2011, pp. 99-103.

P. Blikstein, "Using learning analytics to assess students' behavior in open-ended programming tasks," in Proceedings of the 1st International Conference on Learning Analytics and Knowledge, ACM, Feb 2011, pp. 110-116.

W. Fan, L.Wallace, S. Rich, and Z. Zhang, "Tapping the power of text mining," Communications of the ACM, vol. 49, no. 9, pp. 76-82, 2006.

M. Grobelnik, D. Mladenic, and M. Jermol, "Exploiting text mining in publishing and education," in Proceedings of the ICML-2002 Workshop on Data Mining Lessons Learned, 2002, pp. 34-39.

M. Gamon, A. Aue, S. Corston-Oliver, and E. Ringger, "Pulse: Mining customer opinions from free text," in Proceedings of the International Symposium on Intelligent Data Analysis, Springer Berlin Heidelberg, Sep 2005, pp. 121-132.

N. A. Othman, Prior Educational Experiences and Cultural Factors in the Learners' Attitudes and Behaviours: A Case Study of Distance Learning English Course at UiTM, Malaysia, Vol. 1, PhD Thesis, 2009.

N. Alam Siddiquee, "Public management reform in Malaysia: Recent initiatives and experiences," International Journal of Public Sector Management, vol. 19, no. 4, pp. 339-358, 2006.

A. Che Rozaniza, R. Asbah, and P. Rajalingam, "Promoting positive mental health among students in Malaysia," Psychology and Behavioral Sciences, vol. 2, no. 2, 2013, pp. 73-82. doi: 10.11648/j.pbs.20130202.18.

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