HANDLING OF STUDENT FEEDBACK BASED ON TEXT CLASSIFICATION
DOI:
https://doi.org/10.37569/DalatUniversity.10.3.667(2020)Keywords:
Learner feedback, Naive Bayesian Classification, Support Vector Machine, Text Classification.Abstract
Ensuring quality training has been receiving a lot of attention from university training establishments. Learners play an important role in quality assurance in training and education. To understand the meaning of student feedback on training activities at Nha Trang University (NTU) and to improve the university’s training, we propose to handle student feedback through automatic feedback classification and labeling. The classification and prediction of labels are based on the Support Vector Machine (SVM) and Naive Bayes Classifier (NBC) methods. Experiments with the SVM and NBC methods show positive results, 92.13% and 90.10%, respectively, for the data set of student reviews at Nha Trang University.
Downloads
References
Han, J., Kamber, M., & Pei, J. (2011). Data mining concepts and techniques (3rd ed.). Massachusetts, USA: Morgan Kaufmann Publishing.
Harvard University. (n.d). Getting feedback. Retrieved from https://bokcenter.harvard.edu/getting-feedback.
Hồ, T. T., & Đỗ, P. (2014). Mô hình tích hợp khám phá, phân lớp và gán nhãn chủ đề tiếp cận theo mô hình chủ đề. Tạp chí phát triển KH&CN, 17(K4-2014), 73-85.
Joachims, T. (1998). Text categorization with Support Vector Machines: Learning with many relevant features. Paper presented at The 10th European Conference on Machine Learning (ECML-98), Chemnitz, Germany.
Joachims, T. (1999). Transductive inference for text classification using Support Vector Machines. Paper presented at The 16th International Conference on Machine Learning (ICML’99), San Francisco, USA.
Karthika, S., & Sairam, N. (2015). Naïve Bayesian classifer for educational qualifcation. Indian Journal of Science and Technology, 8(16), 1-5.
L-Università ta’ Malta (UM) (2020). Student feedback. Retrieved from https://www.um.edu.mt/ services/administrativesupport/apqru/studentfeedback.
Maaten, L.V., & Hinton, G. E. (2008). Visualizing data using t-SNE. Journal of Machine Learning Research, 9, 2579-2605.
Nandi, M. (2014). Kernel theory recitation. Pennsylvania, USA: Carnegie Mellon University-Machine Learning Department Publishing.
Robertson, S. E. (2004). Understanding inverse document frequency: On theoretical arguments for IDF. Journal of Documentation, 60(5), 503-520.
Srivastava, D., & Bhambhu, L. (2010). Data classification using Support Vector Machine. Journal of Theoretical and Applied Information Technology, 12(1), 1-7.
Trần, C. Đ., & Phạm, N. K. (2012). Phân loại văn bản với máy học vector hỗ trợ và cây quyết định. Tạp chí Khoa học Trường Đại học Cần Thơ, (21a), 52-63.
Trần, V. T. (2016). Python Vietnamese toolkit. Retrieved from https://pypi.org/project/pyvi/.
Vũ, H. T. (2020). Machine Learning cơ bản. Retrieved from https://github.com/tiepvupsu/ebookMLCB.
Zhang, H. (2004). The optimality of Naive Bayes. Paper presented at The 17th International Florida Artificial Intelligence Research Society Conference, Florida, USA.
Downloads
Published
Volume and Issues
Section
Copyright & License
Copyright (c) 2020 Phạm Thị Kim Ngoan, Nguyễn Hải Triều.
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.