Logistic Regression-based Sentiment Classification Approach for Identifying Undergraduate Student Sentiments in a Course Studied

This study aimed to utilize sentiment classification to ascertain the sentiment of undergraduate students towards the course they have studied. This case study specifically examines the character design course given by the Department of Creative Media, Faculty of Informatics, Mahasarakham University. Unfortunately, our data collection exhibits an imbalance between the positive class and the negative class, with a greater likelihood for the data belong to the positive class. This issue has the potential to result in sentiment classifiers that generate subpar outcomes. Consequently, this issue was also addressed in this study. To develop the binary-based sentiment classifiers, logistic regression methods were employed, specifically traditional logistic regression and logistic regression with class weights. The term weighting scheme is tf-idf, The results were determined to be satisfactory after being evaluated using the F1 score and AUC. However, it was found that the sentiment classifiers generated by L R with class weights showed better results in terms of average F1 score and AUC compared to the sentiment classifiers developed using traditional LR. The overall improvements of F1 score and AUC were 14.51 % and 13.50%, respectively.