Apply Aspect-Based Sentimental Analysis on Course Evaluation

Abstract

Course evaluations provide valuable insights into teaching effectiveness; however, analyzing open-ended feedback is challenging due to its qualitative nature and the scale of the responses. This study employs Aspect-Based Sentiment Analysis (ABSA) to analyze student evaluations from an international undergraduate program, with a focus on the role of data augmentation. We compare three methods: Back-Translation, Paraphrasing, and Generative AI, under transfer and non-transfer learning using BART-Large-CNN and LoRA Llama3.2-3B-Instruct. Models are evaluated with 5-fold cross-validation on both original and augmented datasets. Results show that Back-Translation yields the most consistent improvements for BART-Large-CNN, raising accuracy and F1 by approximately 2%. For Llama, Generative AI performs best in the non-transfer setting, while Back-Translation is more effective with transfer learning. These findings highlight the value of data augmentation in enhancing ABSA for educational feedback and guide on applying NLP to large-scale course evaluation.