Kraisangka J.Noraset T.Kertkeidkachorn N.Mahidol University2026-03-202026-03-202025-01-012025 20th International Joint Symposium on Artificial Intelligence and Natural Language Processing Isai Nlp 2025 (2025)https://repository.li.mahidol.ac.th/handle/123456789/115810Course 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.Computer ScienceEngineeringConference PaperSCOPUS10.1109/iSAI-NLP66160.2025.113207102-s2.0-105032753814