Sentiment Analysis of Student Comment on the College Performance Evaluation Questionnaire Using Naïve Bayes and IndoBERT

Abstract

The development of the Internet has played a significant role in various aspects of life and has generated vast amounts of data, including student comments about universities. The challenge in analyzing comment data is the large number of students providing feedback, which makes manual analysis impractical. The purpose of this study is to analyze the performance evaluation of universities by students in terms of positive and negative sentiments, with the aim of assessing the level of student satisfaction with all elements and areas of university operations. This research utilized the Naïve Bayes algorithm and the IndoBERT model to build a classification model based on questionnaire data, starting from the data collection process, data preprocessing, feature extraction, modeling, and evaluation. The results of the IndoBERT model demonstrated the best performance, with an accuracy of 85%. The IndoBERT model effectively recognizes sentiments in text, distinguishing between positive and negative comments regarding university performance.