{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,20]],"date-time":"2026-07-20T07:02:14Z","timestamp":1784530934212,"version":"3.55.0"},"publisher-location":"Singapore","reference-count":21,"publisher":"Springer Nature Singapore","isbn-type":[{"value":"9789819237609","type":"print"},{"value":"9789819237616","type":"electronic"}],"license":[{"start":{"date-parts":[[2026,7,21]],"date-time":"2026-07-21T00:00:00Z","timestamp":1784592000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2026,7,21]],"date-time":"2026-07-21T00:00:00Z","timestamp":1784592000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2027]]},"DOI":"10.1007\/978-981-92-3761-6_12","type":"book-chapter","created":{"date-parts":[[2026,7,20]],"date-time":"2026-07-20T06:13:24Z","timestamp":1784528004000},"page":"166-179","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["An Empirical Study on Student Feedback Sentiment Classification: From Traditional Machine Learning to Large Language Models"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0009-0001-6819-3836","authenticated-orcid":false,"given":"Hongjie","family":"Zhang","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0965-3617","authenticated-orcid":false,"given":"Haoran","family":"Xie","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8435-9739","authenticated-orcid":false,"given":"Di","family":"Zou","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3976-0053","authenticated-orcid":false,"given":"Fu Lee","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,7,21]]},"reference":[{"key":"12_CR1","doi-asserted-by":"publisher","DOI":"10.1016\/j.nlp.2023.100009","volume":"3","author":"L Xu","year":"2023","unstructured":"Xu, L., Wang, W.: Improving aspect-based sentiment analysis with contrastive learning. Natur. Lang. Proc. J. 3, 100009 (2023)","journal-title":"Natur. Lang. Proc. J."},{"issue":"1","key":"12_CR2","doi-asserted-by":"publisher","first-page":"69","DOI":"10.1109\/MIS.2024.3508432","volume":"40","author":"L Xu","year":"2025","unstructured":"Xu, L., Xie, H., Qin, S.J., Wang, F.L., Tao, X.: Exploring ChatGPT-based augmentation strategies for contrastive aspect-based sentiment analysis. IEEE Intell. Syst. 40(1), 69\u201376 (2025)","journal-title":"IEEE Intell. Syst."},{"issue":"8","key":"12_CR3","doi-asserted-by":"publisher","first-page":"1735","DOI":"10.1162\/neco.1997.9.8.1735","volume":"9","author":"S Hochreiter","year":"1997","unstructured":"Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural Comput. 9(8), 1735\u20131780 (1997)","journal-title":"Neural Comput."},{"key":"12_CR4","doi-asserted-by":"crossref","unstructured":"Kim, Y.: Convolutional Neural Networks for Sentence Classification. Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), 1746\u201351 (2014)","DOI":"10.3115\/v1\/D14-1181"},{"key":"12_CR5","unstructured":"Devlin, J., Chang, M.W., Lee, K., Toutanova, K.: Bert: pre-training of deep bidirectional transformers for language understanding. Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 4171\u201386 (2019)"},{"key":"12_CR6","unstructured":"Liu, Y., Ott, M., Goyal, N.. et al.: Roberta: A Robustly Optimized Bert Pretraining Approach. arXiv Preprint arXiv:1907.11692 (2019)"},{"key":"12_CR7","unstructured":"OpenAI. Introducing GPT-4.1 in the API. https:\/\/openai.com\/index\/gpt-4-1\/. (2025)"},{"key":"12_CR8","unstructured":"Meta Llama. Meta-Llama\/Llama-3.3\u201370B-Instruct. https:\/\/huggingface.co\/meta-llama\/Llama-3.3-70B-Instruct (2024)"},{"issue":"1","key":"12_CR9","doi-asserted-by":"publisher","first-page":"6","DOI":"10.1186\/s41039-018-0073-0","volume":"13","author":"S Gottipati","year":"2018","unstructured":"Gottipati, S., Shankararaman, V., Lin, J.R.: Text analytics approach to extract course improvement suggestions from students\u2019 feedback. Res. Pract. Technol. Enhanc. Learn. 13(1), 6 (2018)","journal-title":"Res. Pract. Technol. Enhanc. Learn."},{"issue":"3","key":"12_CR10","doi-asserted-by":"publisher","first-page":"647","DOI":"10.1007\/s12528-023-09370-5","volume":"36","author":"C Grimalt-\u00c1lvaro","year":"2024","unstructured":"Grimalt-\u00c1lvaro, C., Usart, M.: Sentiment analysis for formative assessment in higher education: a systematic literature review. J. Comput. High. Educ. 36(3), 647\u2013682 (2024)","journal-title":"J. Comput. High. Educ."},{"key":"12_CR11","doi-asserted-by":"crossref","unstructured":"Herath, M., Chamindu, K., Maduwantha, H., Ranathunga, S.: Dataset and Baseline for Automatic Student Feedback Analysis. Proceedings of the Thirteenth Language Resources and Evaluation Conference, pp. 2042\u201349 (2022)","DOI":"10.63317\/5m9fbbuupyvy"},{"issue":"4","key":"12_CR12","doi-asserted-by":"publisher","first-page":"2061","DOI":"10.3390\/app13042061","volume":"13","author":"M Fargues","year":"2023","unstructured":"Fargues, M., Kadry, S., Lawal, I.A., Yassine, S., Rauf, H.T.: Automated analysis of open-ended students\u2019 feedback using sentiment, emotion, and cognition classifications. Appl. Sci. 13(4), 2061 (2023)","journal-title":"Appl. Sci."},{"issue":"1","key":"12_CR13","doi-asserted-by":"publisher","first-page":"2","DOI":"10.1007\/s44443-025-00018-1","volume":"37","author":"WA Awadh","year":"2025","unstructured":"Awadh, W.A., Sulaiman, R.B., Mahmoud, M.A.: Aspect-based sentiment analysis in MOOCs: a systematic literature review introducing the MASC-MEF framework. J. King Saud Univ. Comp. Info. Sci. 37(1), 2 (2025)","journal-title":"J. King Saud Univ. Comp. Info. Sci."},{"key":"12_CR14","doi-asserted-by":"crossref","unstructured":"Tang, D., Qin, B., Liu, T.: Document modeling with gated recurrent neural network for sentiment classification. Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing, pp. 1422\u201332 (2015)","DOI":"10.18653\/v1\/D15-1167"},{"key":"12_CR15","first-page":"1877","volume":"33","author":"T Brown","year":"2020","unstructured":"Brown, T., Mann, B., Ryder, N., et al.: Language models are few-shot learners. Adv. Neural. Inf. Process. Syst. 33, 1877\u20131901 (2020)","journal-title":"Adv. Neural. Inf. Process. Syst."},{"key":"12_CR16","doi-asserted-by":"crossref","unstructured":"Min, S., Lyu, X., Holtzman A., et al.: Rethinking the role of demonstrations: what makes in-context learning work? Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing, pp. 11048\u201364 (2022)","DOI":"10.18653\/v1\/2022.emnlp-main.759"},{"key":"12_CR17","doi-asserted-by":"crossref","unstructured":"Liu, J., Shen, D., Zhang, Y., Dolan, B., Carin, L., Chen, W.: What Makes Good in-Context Examples for GPT-3?\u201d Proceedings of Deep Learning Inside Out (DeeLIO 2022): The 3rd Workshop on Knowledge Extraction and Integration for Deep Learning Architectures (Dublin, Ireland; Online), pp. 100\u2013114 (2022)","DOI":"10.18653\/v1\/2022.deelio-1.10"},{"key":"12_CR18","unstructured":"Zhao, Z., Wallace, E., Feng, S., Klein, D., Singh, S.: Calibrate before use: improving few-shot performance of language models. Proceedings of the 38th International Conference on Machine Learning, Proceedings of machine learning research, 139, 12697\u2013706 (2021)"},{"key":"12_CR19","first-page":"9647","volume":"2023","author":"S Gretz","year":"2023","unstructured":"Gretz, S., Halfon, A., Shnayderman, I., et al.: Zero-shot topical text classification with LLMs-an experimental study. Findings of the Association for Computational Linguistics: EMNLP 2023, 9647\u20139676 (2023)","journal-title":"Findings of the Association for Computational Linguistics: EMNLP"},{"key":"12_CR20","doi-asserted-by":"crossref","unstructured":"Edwards, A., Camacho-Collados, J.: Language Models for Text Classification: Is in-Context Learning Enough? Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024), pp. 10058\u201372 (2024)","DOI":"10.63317\/3dnziwdd5bh2"},{"key":"12_CR21","doi-asserted-by":"publisher","first-page":"24824","DOI":"10.52202\/068431-1800","volume":"35","author":"J Wei","year":"2022","unstructured":"Wei, J., Wang, X., Schuurmans, D., et al.: Chain-of-thought prompting elicits reasoning in large language models. Adv. Neural. Inf. Process. Syst. 35, 24824\u201324837 (2022)","journal-title":"Adv. Neural. Inf. Process. Syst."}],"container-title":["Lecture Notes in Computer Science","Blended Learning. Innovations for Future Education"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-981-92-3761-6_12","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,7,20]],"date-time":"2026-07-20T06:13:29Z","timestamp":1784528009000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-981-92-3761-6_12"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,7,21]]},"ISBN":["9789819237609","9789819237616"],"references-count":21,"URL":"https:\/\/doi.org\/10.1007\/978-981-92-3761-6_12","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,7,21]]},"assertion":[{"value":"21 July 2026","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"It is now necessary to declare any competing interests or to specifically state that the authors have no competing interests.","order":1,"name":"Ethics","label":"Disclosure of Interests.","group":{"name":"EthicsHeading","label":"Ethics"}},{"value":"ICBL","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Blended Learning","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Beijing","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"China","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2026","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"28 July 2026","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"31 July 2026","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"19","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"icbl2026","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/hksmic.org.hk\/icbl\/2026\/index.html","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}