Student Satisfaction Classification Algorithm Using the Minority Synthetic Oversampling Technique

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.