TY - JOUR
TI - Aspect-based sentiment analysis of open-ended responses in student course evaluation surveys
AU - Alotaibi Reem
JN - Thermal Science
PY - 2024
VL - 28
IS - 6
SP - 5037
EP - 5047
PT - Article
AB - Student surveys are tools used to gather feedback and opinions from students  regarding various aspects of their educational experience such as  satisfaction with courses, instructors, supervision, facilities, services,  and overall campus environment. These surveys play a crucial role to assess  and improve the quality of education and overall student experience, and  provide insights that can be used to decision makers. There are two primary  categories of survey questions: open-ended and closed-ended. Open-ended  questions allow respondents to provide detailed answers in their own words,  while closed-ended questions offer only predetermined options. For analyzing  student surveys, it is common practice to focus on closed-ended questions,  while often open-ended responses provide valuable insights. In this paper,  we use sentiment analysis to extract students' emotions towards various  aspects of their educational experience using transformer-based pre-trained  language models. The results show that the multilingual XLM-RoBERTa  demonstrated encouraging results compared to the multilingual BERT model  with an accuracy of 0.81 and F1 of 0.80 for the classification of aspects  while for the classification of sentiments, obtained 0.94 and 0.92 for  accuracy and F1, respectively.