Sentiment analysis of student evaluation feedback using transformer-based language models

This paper proposes an approach to sentiment analysis of student evaluation feedback using transformer-based language models. The primary objective of this study is to conduct an in-depth analysis of sentiment expressed in student evaluation feedback, with a focus on introducing contextual understanding into the sentiment classification process. In this research, four different variants of transformer language models were assessed, namely multilingual bidirectional encoder representations from transformers (MBERT), IndoBERT, RoBERTa Indonesia, and generative pre-trained transformer (GPT-2 Indonesia). Additionally, we also compared the performance of transformer models with two traditional models, namely support vector machine (SVM) and Naive Bayes (NB). The evaluation was conducted using feedback data collected from the Evaluasi Dosen oleh Mahasiswa (EDOM) system at Riau University, which had been categorized as either positive or negative. The outcomes indicate that IndoBERT base uncased exhibits the highest performance, with precision, accuracy, and recall values of 0.858, 0.929, and 0.911, respectively. This observation highlights the effectiveness of transformer-based language models in sentiment analysis of student evaluation feedback and provides insights for improving educational assessment practices.