{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,9]],"date-time":"2026-07-09T14:24:48Z","timestamp":1783607088442,"version":"3.55.0"},"reference-count":45,"publisher":"Association for Computing Machinery (ACM)","issue":"5","license":[{"start":{"date-parts":[[2026,6,30]],"date-time":"2026-06-30T00:00:00Z","timestamp":1782777600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/legalcode"}],"funder":[{"DOI":"10.13039\/501100012166","name":"National Key R&D Program of China","doi-asserted-by":"crossref","award":["SQ2024YFB4500127 and 2025YFE0213400"],"award-info":[{"award-number":["SQ2024YFB4500127 and 2025YFE0213400"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"crossref"}]},{"name":"Hubei Provincial Natural Science Foundation of China","award":["Grant 2026AFA107 and 2026AFB386"],"award-info":[{"award-number":["Grant 2026AFA107 and 2026AFB386"]}]},{"DOI":"10.13039\/501100002858","name":"China Postdoctoral Science Foundation","doi-asserted-by":"crossref","award":["Grant GZB20240244"],"award-info":[{"award-number":["Grant GZB20240244"]}],"id":[{"id":"10.13039\/501100002858","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/501100002858","name":"China Postdoctoral Science Foundation","doi-asserted-by":"crossref","award":["Grant 2024M761016"],"award-info":[{"award-number":["Grant 2024M761016"]}],"id":[{"id":"10.13039\/501100002858","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/501100021171","name":"Guangdong Basic and Applied Basic Research Foundation","doi-asserted-by":"crossref","award":["Grant 2024A1515030017"],"award-info":[{"award-number":["Grant 2024A1515030017"]}],"id":[{"id":"10.13039\/501100021171","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["ACM Trans. Inf. Syst."],"published-print":{"date-parts":[[2026,6,30]]},"abstract":"<jats:p>Recently, generative aspect-level sentiment quadruple prediction (ASQP) methods based on pre-trained language models have made significant progress. However, some challenges remain in extracting and recognizing complex sentiment elements from semantically rich sentences, limiting the generalization and adaptability of unidirectional generative models in aspect-level sentiment analysis. To overcome this limitation, this article proposes a Generative Aspect-Based Sentiment Quadruple Prediction Model based on Multi-Order Prompting (GenMOP). The model draws on the concept of prompt learning and introduces a multi-order prompting strategy, which breaks the traditional framework of a single generative order and enhances the flexibility and adaptability of the model. Furthermore, we integrate a quadruple quantity-aware module and a multi-view uncertainty-aware module based on a basic generative architecture, not only providing the model with more fine-grained information about the quadruple quantity but also improving the prediction accuracy through uncertainty estimation. The extensive experiments show that the GenMOP method achieves excellent performance in the ASQP task. On the four benchmark datasets including Rest15, Rest16, Rest and Lap, our model achieves F1 score improvements of 1.26%, 0.23%, 0.28%, and 2.07%, respectively, compared to existing state-of-the-arts, demonstrating its effectiveness and superiority in dealing with the joint extraction of multiple sentiment elements of the ASQP model.<\/jats:p>","DOI":"10.1145\/3811909","type":"journal-article","created":{"date-parts":[[2026,4,27]],"date-time":"2026-04-27T12:27:48Z","timestamp":1777292868000},"page":"1-29","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Generative Aspect-Based Sentiment Quadruple Prediction Based on Multi-Order Prompting"],"prefix":"10.1145","volume":"44","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-3358-2708","authenticated-orcid":false,"given":"Rui","family":"Wang","sequence":"first","affiliation":[{"name":"School of Computer Science and Technology, Huazhong University of Science and Technology, Wuhan, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0083-7332","authenticated-orcid":false,"given":"Muyao","family":"He","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology, Huazhong University of Science and Technology, Wuhan, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0006-1636-9437","authenticated-orcid":false,"given":"Wenjie","family":"Liu","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology, Huazhong University of Science and Technology, Wuhan, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7296-2522","authenticated-orcid":false,"given":"Yixue","family":"Hao","sequence":"additional","affiliation":[{"name":"Huazhong University of Science and Technology, Wuhan, China and Guangdong Provincial Key Laboratory of Manufacturing Equipment Digitization, Guangdong HUST Industrial Technology Research Institute, Dongguan, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6496-6793","authenticated-orcid":false,"given":"Long","family":"Hu","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology, Huazhong University of Science and Technology, Wuhan, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0960-4447","authenticated-orcid":false,"given":"Min","family":"Chen","sequence":"additional","affiliation":[{"name":"School of Computer Science and Engineering, South China University of Technology, Guangzhou, China and Pazhou Laboratory, Guangzhou, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4421-652X","authenticated-orcid":false,"given":"Baoru","family":"Huang","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Informatics, University of Liverpool, Liverpool, United Kingdom and The Hamlyn Centre for Robotic Surgery, Imperial College London, London, United Kingdom"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2026,7,9]]},"reference":[{"key":"e_1_3_1_2_2","doi-asserted-by":"publisher","DOI":"10.1145\/2950050"},{"key":"e_1_3_1_3_2","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2023.findings-acl.505"},{"key":"e_1_3_1_4_2","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2021.acl-long.29"},{"key":"e_1_3_1_5_2","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2020.acl-main.582"},{"key":"e_1_3_1_6_2","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2021.naacl-main.146"},{"key":"e_1_3_1_7_2","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2022.acl-long.26"},{"key":"e_1_3_1_8_2","doi-asserted-by":"publisher","DOI":"10.1145\/3564281"},{"key":"e_1_3_1_9_2","first-page":"701","volume-title":"Proceedings of 22nd Chinese National Conference on Computational Linguistics","author":"Fuyao Zhang","year":"2023","unstructured":"Zhang Fuyao, Zhang Yijia, Wang Mengyi, Yang Hong, Lu Mingyu, and Yang Liang. 2023. Self question-answering: Aspect sentiment triplet extraction via a multi-MRC framework based on rethink mechanism. In Proceedings of 22nd Chinese National Conference on Computational Linguistics, 701\u2013712."},{"key":"e_1_3_1_10_2","first-page":"7002","volume-title":"Proceedings of 29th International Conference on Computational Linguistics","author":"Gao Tianhao","year":"2022","unstructured":"Tianhao Gao, Jun Fang, Hanyu Liu, Zhiyuan Liu, Chao Liu, Pengzhang Liu, Yongjun Bao, and Weipeng Yan. 2022. LEGO-ABSA: A prompt-based task assemblable unified generative framework for multi-task aspect-based sentiment analysis. 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