Using Opinion Mining in Student Assessments to Improve Teaching Quality in Universities

School dropout is a major challenge that should be mitigated by universities. Early identification of students’ dissatisfaction allows detecting problems in institutions, being useful for the decision-making process. In this context, opinion mining algorithms can help in the identification of students’ opinions in institutional evaluation surveys. In this paper, we analyzed the answers of questionnaires carried out at a public university from 2012 to 2018 to capture students’ opinions automatically through a sentiment classifier. We evaluated classifiers such as SVM, Naive Bayes, Logistic Regression, and Neural Networks. The results of the best classifier indicate an accuracy of 87% for the classification task.