Manuscript received December 23, 2022; revised February 2, 2023; accepted February 15, 2023.
Abstract—This study is based on the university students’
opinions on the social network Twitter, to learn the teaching
performance in the context of virtual learning using sentiment
analysis technique. However, to establishing the classification
algorithm, an imbalance was evidenced in the amounts of
opinions that qualify the teaching performance with the
satisfied and dissatisfied class. Therefore, the objective of this
investigation is to determine the improvement in the
performance of the student satisfaction classification algorithm,
based on the class balancing method from the application of the
minority synthetic oversampling technique (SMOTE). From the
methodological point of view, the research is a
non-experimental design, applied type, and quantitative
approach. The data was collected through the social network
Twitter for fifteen weeks to a population defined by mechanical
and electrical engineering students. After the application of the
SMOTE data balancing technique, it was identified that the
algorithm which presents the best performance is Logistic
Regression. It was possible to identify that the impact of
improvement of the algorithm turned out to be an average of
2.17% in the accuracy, 84.78% in precision, 42% in the Recall
(Sensitivity) and 58.33% in the F1-score. Therefore, it is
demonstrated that the algorithm classifies with high probability
the opinions of the students.
Index Terms—Performance, classification algorithm, student
satisfaction, teacher performance, oversampling
Omar Chamorro-Atalaya is with the Faculty of Engineering and
Management of the National Technological University of South Lima, Lima,
Peru.
Florcita Aldana-Trejo is with the Faculty of Economic Sciences of the
Federico Villarreal National University, Lima, Peru.
Nestor Alvarado-Bravo, Constantino Nieves-Barreto, and Santiago
Aguilar-Loyaga are with the Faculty of Administrative Sciences of the
National University of Callao, Lima, Peru.
José Farfán-Aguilar is with the Faculty of Industrial Engineering and
Systems of the National University of Callao, Lima, Peru.
Almintor Torres-Quiroz is with the Faculty of Economic Sciences of the
National University of Callao, Lima, Peru.
Alípio Riveros-Cuellar is with the Faculty of Administrative Sciences of
the National University Federico Villarreal, Lima, Peru.
Manuel Pérez-Samanamud and Luciano Pérez-Guevara are with the
Faculty of Education of the National University Federico Villarreal, Lima,
Peru.
*Correspondence: ochamorro@untels.edu.pe (O.C.A.)
Cite: Omar Chamorro-Atalaya*, Florcita Aldana-Trejo, Nestor Alvarado-Bravo, Constantino Nieves-Barreto, Santiago Aguilar-Loyaga, José Farfán-Aguilar, Almintor Torres-Quiroz, Alípio Riveros-Cuellar, Manuel Pérez-Samanamud, and Luciano Pérez-Guevara, "Student Satisfaction Classification Algorithm Using the Minority Synthetic Oversampling Technique," International Journal of Information and Education Technology vol. 13, no. 7, pp. 1094-1100, 2023.
Copyright © 2023 by the authors. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).