Apply Aspect-Based Sentimental Analysis on Course Evaluation
Issued Date
2025-01-01
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Scopus ID
2-s2.0-105032753814
Journal Title
2025 20th International Joint Symposium on Artificial Intelligence and Natural Language Processing Isai Nlp 2025
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SCOPUS
Bibliographic Citation
2025 20th International Joint Symposium on Artificial Intelligence and Natural Language Processing Isai Nlp 2025 (2025)
Suggested Citation
Kraisangka J., Noraset T., Kertkeidkachorn N. Apply Aspect-Based Sentimental Analysis on Course Evaluation. 2025 20th International Joint Symposium on Artificial Intelligence and Natural Language Processing Isai Nlp 2025 (2025). doi:10.1109/iSAI-NLP66160.2025.11320710 Retrieved from: https://repository.li.mahidol.ac.th/handle/123456789/115810
Title
Apply Aspect-Based Sentimental Analysis on Course Evaluation
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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.