方面级情感分析研究综述

doi:10.3778/j.issn.1002-8331.2409-0258

摘要/Abstract

摘要： 随着情感分析的不断发展，提出了侧重于对评价对象及其属性进行细粒度情感分析的方面级情感分析，受到了研究者越来越多的关注。与传统的情感分析相比，方面级情感分析能够准确地反映评价对象及其意见，帮助企业有针对性地改进，助力企业高质量发展。根据使用方法的不同，将其划分为基于规则的方法、基于注意力的方法、基于机器阅读理解的方法、基于序列标注的方法以及基于生成的方法，并对现有的文献进行了分析和总结；介绍方面级情感分析的新任务：多模态、隐式和跨领域方面级情感分析；给出了常用数据资源，如SemEval系列、MAMS、ACOS和CMU-MOSEI；对面临的挑战和未来的研究方向进行了探讨，指出需要解决数据量小、领域单一、数据质量难以评估的问题，加强对复杂语句和多模态的方面级情感分析，如何更好地利用预训练语言模型为更深入的研究提供思路。

关键词: 机器学习, 深度学习, 方面级情感分析

Abstract: With the continuous development of the sentiment analysis, aspect-based sentiment analysis (ABSA) is proposed, which is an analytical method that focuses on analyzing the evaluated object and its attributes. ABSA has received increasing attention. Compared with traditional sentiment analysis, ABSA can accurately reflect the evaluated aspect and its opinions, helping enterprises make targeted improvements and promote high-quality development. According to different methods, they can be divided into rule-based methods, attention based methods, machine reading comprehension based methods, sequence annotation based methods, and generative based methods. Existing research is analyzed and summarized. In addition, this paper also introduces new tasks in ABSA：multimodal, implicit, and cross-domain aspect-based sentiment analysis; provides common data resources such as SemEval series, MAMS, ACOS, and CMU-MOSEI. Finally, this paper explores the challenges faced and future research directions, such as small data volumes, single domains, and unevaluable data quality. It emphasizes the importance of enhancing aspect-based sentiment analysis for complex sentences and multimodal contexts, and explores how to better leverage pre-trained language models to provide insights for more in-depth research.

Key words: machine learning, deep learning, aspect-based sentiment analysis

徐放, 尹伊纯, 吴方君. 方面级情感分析研究综述[J]. 计算机工程与应用, 2025, 61(19): 60-73.

XU Fang, YIN Yichun, WU Fangjun. Survey on Aspect-Based Sentiment Analysis[J]. Computer Engineering and Applications, 2025, 61(19): 60-73.

参考文献

[1] 周建, 刘炎宝, 刘佳佳. 情感分析研究的知识结构及热点前沿探析[J]. 情报学报, 2020, 39(1): 111-124.
ZHOU J, LIU Y B, LIU J J. Exploration of intellectual structure and hot issues in sentiment analysis research[J]. Journal of the China Society for Scientific and Technical Information, 2020, 39(1): 111-124.
[2] 唐晓波, 刘广超. 细粒度情感分析研究综述[J]. 图书情报工作, 2017, 61(5): 132-140.
TANG X B, LIU G C. Research review on fine-grained sentiment analysis[J]. Library and Information Service, 2017, 61(5): 132-140.
[3] ZHANG W X, LI X, DENG Y, et al. A survey on aspect-based sentiment analysis: tasks, methods, and challenges[J]. IEEE Transactions on Knowledge and Data Engineering, 2023, 35(11): 11019-11038.
[4] 陈壮, 钱铁云, 李万理, 等. 低资源方面级情感分析研究综述[J]. 计算机学报, 2023, 46(7): 1445-1472.
CHEN Z, QIAN T Y, LI W L, et al. Low-resource aspect-based sentiment analysis: a survey[J]. Chinese Journal of Computers, 2023, 46(7): 1445-1472.
[5] HU M Q, LIU B. Mining and summarizing customer reviews[C]//Proceedings of the 10th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. New York: ACM, 2004: 168-177.
[6] 潘美琦, 马致远, 刘高飞, 等. 端到端方面级情感分析综述[J]. 小型微型计算机系统, 2024, 45(3): 732-746.
PAN M Q, MA Z Y, LIU G F, et al. Survey on end-to-end aspect-based sentiment analysis[J]. Journal of Chinese Computer Systems, 2024, 45(3): 732-746.
[7] 李阳, 王石, 朱俊武, 等. 方面级情感分析综述[J]. 计算机科学, 2023, 50(S1): 24-30.
LI Y, WANG S, ZHU J W, et al. Summarization of aspect-level sentiment analysis[J]. Computer Science, 2023, 50(S1): 24-30.
[8] GUNES O, FURCHE T, ORSI G. Structured aspect extraction[C]//Proceedings of the 26th International Conference on Computational Linguistics: Technical Papers, 2016: 2321-2332.
[9] SHAFIE A S, SHAREF N M, AZMI MURAD M A, et al. Aspect extraction performance with POS tag pattern of dependency relation in aspect-based sentiment analysis[C]//Proceedings of the 2018 4th International Conference on Information Retrieval and Knowledge Management. Piscataway: IEEE, 2018: 1-6.
[10] KARAO?LAN K M, FINDIK O. Extended rule-based opinion target extraction with a novel text pre-processing method and ensemble learning[J]. Applied Soft Computing, 2022, 118: 108524.
[11] KUMAR A, SAINI M, SHARAN A. Aspect category detection using statistical and semantic association[J]. Computational Intelligence, 2020, 36(3): 1161-1182.
[12] GINDL S, WEICHSELBRAUN A, SCHARL A. Rule-based opinion target and aspect extraction to acquire affective knowledge[C]//Proceedings of the 22nd International Conference on World Wide Web. New York: ACM, 2013: 557-564.
[13] DINSOREANU M, BACU A. Unsupervised Twitter sentiment classification[C]//Proceedings of the International Conference on Knowledge Management and Information Sharing, 2014: 220-227.
[14] 赵妍妍, 秦兵, 车万翔, 等. 基于句法路径的情感评价单元识别[J]. 软件学报, 2011, 22(5): 887-898.
ZHAO Y Y, QIN B, CHE W X, et al. Appraisal expression recognition based on syntactic path[J]. Journal of Software, 2011, 22(5): 887-898.
[15] KHOT G D, TIRMARE H A. Detecting a specific aspect category for sentiment analysis using association rule mining scheme[C]//Proceedings of the 2018 4th International Conference on Computing Communication Control and Automation. Piscataway: IEEE, 2018: 1-7.
[16] HE R D, LEE W S, NG H T, et al. An unsupervised neural attention model for aspect extraction[C]//Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics. Stroudsburg: ACL, 2017: 388-397.
[17] CHAKRABORTY A. Aspect and opinion term extraction using graph attention network[C]//Proceedings of the 20th International Conference on Natural Language Processing, 2023: 594-602.
[18] JIANG J F, WANG A, AIZAWA A. Attention-based relational graph convolutional network for target-oriented opinion words extraction[C]//Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics. Stroudsburg: ACL, 2021: 1986-1997.
[19] MOVAHEDI S, GHADERY E, FAILI H, et al. Aspect category detection via topic-attention network[J]. arXiv:1901.
01183, 2019.
[20] ZHAO P L, HOU L L, WU O. Modeling sentiment dependencies with graph convolutional networks for aspect-level sentiment classification[J]. Knowledge-Based Systems, 2020, 193: 105443.
[21] 张瑾, 段利国, 李爱萍, 等. 基于注意力与门控机制相结合的细粒度情感分析[J]. 计算机科学, 2021, 48(8): 226-233.
ZHANG J, DUAN L G, LI A P, et al. Fine-grained sentiment analysis based on combination of attention and gated mechanism[J]. Computer Science, 2021, 48(8): 226-233.
[22] XU G T, LIU P Y, ZHU Z F, et al. Attention-enhanced graph convolutional networks for aspect-based sentiment classification with multi-head attention[J]. Applied Sciences, 2021, 11(8): 3640.
[23] ZHANG Z, ZHOU Z L, WANG Y N. SSEGCN: syntactic and semantic enhanced graph convolutional network for aspect-based sentiment analysis[C]//Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies. Stroudsburg: ACL, 2022: 4916-4925.
[24] MA F K, HU X M, LIU A W, et al. AMR-based network for aspect-based sentiment analysis[C]//Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics. Stroudsburg: ACL, 2023: 322-337.
[25] 谢泽, 陈庆锋, 莫少聪, 等. 知识增强的双通道多头GCN用于方面级情感分析[J]. 计算机工程与应用, 2024, 60(17): 98-106.
XIE Z, CHEN Q F, MO S C, et al. Knowledge enhanced dual-channel multi-head graph convolutional networks for aspect-based sentiment analysis[J]. Computer Engineering and Applications, 2024, 60(17): 98-106.
[26] WANG W Y, PAN S J, DAHLMEIER D, et al. Coupled multi-layer attentions for co-extraction of aspect and opinion terms[J]. Proceedings of the AAAI Conference on Artificial Intelligence, 2017, 31(1): 3316-3322.
[27] YANG J, YANG J. Aspect based sentiment analysis with self-attention and gated convolutional networks[C]//Proceedings of the 2020 IEEE 11th International Conference on Software Engineering and Service Science. Piscataway: IEEE, 2020: 146-149.
[28] MAO Y, SHEN Y, YU C, et al. A joint training dual-MRC framework for aspect based sentiment analysis[J]. Proceedings of the AAAI Conference on Artificial Intelligence, 2021, 35(15): 13543-13551.
[29] GAO L, WANG Y L, LIU T C, et al. Question-driven span labeling model for aspect-opinion pair extraction[J]. Proceedings of the AAAI Conference on Artificial Intelligence, 2021, 35(14): 12875-12883.
[30] LIU S, LI K W, LI Z H. A robustly optimized BMRC for aspect sentiment triplet extraction[C]//Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies. Stroudsburg: ACL, 2022: 272-278.
[31] WEI Z K, HONG Y, ZOU B W, et al. Don’t eclipse your arts due to small discrepancies: boundary repositioning with a pointer network for aspect extraction[C]//Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics. Stroudsburg: ACL, 2020: 3678-3684.
[32] MENSAH S, SUN K, ALETRAS N. An empirical study on leveraging position embeddings for target-oriented opinion words extraction[C]//Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing. Stroudsburg: ACL, 2021: 9174-9179.
[33] MENSAH S, SUN K, ALETRAS N. Trading syntax trees for wordpieces: target-oriented opinion words extraction with wordpieces and aspect enhancement[C]//Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics. Stroudsburg: ACL, 2023: 999-1007.
[34] WU S Q, FEI H, REN Y F, et al. Learn from syntax: improving pair-wise aspect and opinion terms extraction with rich syntactic knowledge[C]//Proceedings of the 30th International Joint Conference on Artificial Intelligence, 2021: 3957-3963.
[35] CAI H J, TU Y F, ZHOU X S, et al. Aspect-category based sentiment analysis with hierarchical graph convolutional network[C]//Proceedings of the 28th International Conference on Computational Linguistics. International Committee on Computational Linguistics, 2020: 833-843.
[36] WU Z, YING C C, ZHAO F, et al. Grid tagging scheme for aspect-oriented fine-grained opinion extraction[C]//Findings of the Association for Computational Linguistics: EMNLP 2020. Stroudsburg: ACL, 2020: 2576-2585.
[37] WU C, XIONG Q Y, YI H L, et al. Multiple-element joint detection for aspect-based sentiment analysis[J]. Knowledge-Based Systems, 2021, 223: 107073.
[38] CHEN Z X, HUANG H, LIU B, et al. Semantic and syntactic enhanced aspect sentiment triplet extraction[C]//Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021. Stroudsburg: ACL, 2021: 1474-1483.
[39] CHEN H, ZHAI Z P, FENG F X, et al. Enhanced multi-channel graph convolutional network for aspect sentiment triplet extraction[C]//Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics. Stroudsburg: ACL, 2022: 2974-2985.
[40] ZHANG Y C, YANG Y F, LI Y H, et al. Boundary-driven table-filling for aspect sentiment triplet extraction[C]//Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing. Stroudsburg: ACL, 2022: 6485-6498.
[41] SU G X, WU M M, HUANG Z Q, et al. Refine, align, and aggregate: multi-view linguistic features enhancement for aspect sentiment triplet extraction[C]//Findings of the Association for Computational Linguistics ACL 2024. Stroudsburg: ACL, 2024: 3212-3228.
[42] ZHOU J X, YANG H Q, HE Y X, et al. A unified one-step solution for aspect sentiment quad prediction[C]//Findings of the Association for Computational Linguistics: ACL 2023. Stroudsburg: ACL, 2023: 12249-12265.
[43] LIU J, TENG Z Y, CUI L Y, et al. Solving aspect category sentiment analysis as a text generation task[C]//Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing. Stroudsburg: ACL, 2021: 4406-4416.
[44] TRAN T, SHIRAI K, KERTKEIDKACHORN N. Text generation model enhanced with semantic information in aspect category sentiment analysis[C]//Findings of the Association for Computational Linguistics: ACL 2023. Stroudsburg: ACL, 2023: 5256-5268.
[45] MUKHERJEE R, NAYAK T, BUTALA Y, et al. PASTE: a tagging-free decoding framework using pointer networks for aspect sentiment triplet extraction[C]//Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing. Stroudsburg: ACL, 2021: 9279-9291.
[46] FEI H, REN Y F, ZHANG Y, et al. Nonautoregressive encoder-decoder neural framework for end-to-end aspect-based sentiment triplet extraction[J]. IEEE Transactions on Neural Networks and Learning Systems, 2023, 34(9): 5544-5556.
[47] MUKHERJEE R, KANNEN N, PANDEY S, et al. CONTRASTE: supervised contrastive pre-training with aspect-based prompts for aspect sentiment triplet extraction[C]//Findings of the Association for Computational Linguistics: EMNLP 2023. Stroudsburg: ACL, 2023: 12065-12080.
[48] LUO X L, YANG M, WANG Y H. Tagging-assisted generation model with encoder and decoder supervision for aspect sentiment triplet extraction[C]//Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing. Stroudsburg: ACL, 2023: 2078-2093.
[49] ZHOU S, QIAN T Y. On the strength of sequence labeling and generative models for aspect sentiment triplet extraction[C]//Findings of the Association for Computational Linguistics: ACL 2023. Stroudsburg: ACL, 2023: 12038-12050.
[50] LV H R, LIU J Y, WANG H N, et al. Efficient hybrid generation framework for aspect-based sentiment analysis[C]//Proceedings of the 17th Conference of the European Chapter of the Association for Computational Linguistics. Stroudsburg: ACL, 2023: 1007-1018.
[51] ZHU L N, CHEN X F, GUO X L, et al. Pinpointing diffusion grid noise to enhance aspect sentiment quad prediction[C]//Findings of the Association for Computational Linguistics ACL 2024. Stroudsburg: ACL, 2024: 3717-3726.
[52] ZHANG W X, DENG Y, LI X, et al. Aspect sentiment quad prediction as paraphrase generation[C]//Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing. Stroudsburg: ACL, 2021: 9209-9219.
[53] PEPER J, WANG L. Generative aspect-based sentiment analysis with contrastive learning and expressive structure[C]//Findings of the Association for Computational Linguistics: EMNLP 2022. Stroudsburg: ACL, 2022: 6089-6095.
[54] BAI Y H, XIE Y L, LIU X Y, et al. BvSP: broad-view soft prompting for few-shot aspect sentiment quad prediction[C]//Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics. Stroudsburg: ACL, 2024: 8465-8482.
[55] 彭文忠, 夏家莉, 万齐智, 等. 基于完形填空的方面级情感四元组预测[J]. 计算机学报, 2024, 47(8): 1744-1768.
PENG W Z, XIA J L, WAN Q Z, et al. Modeling aspect sentiment quad prediction as cloze task[J]. Chinese Journal of Computers, 2024, 47(8): 1744-1768.
[56] YAN H, DAI J Q, JI T, et al. A unified generative framework for aspect-based sentiment analysis[C]//Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing. Stroudsburg: ACL, 2021: 2416-2429.
[57] MAO Y, SHEN Y, YANG J C, et al. Seq2Path: generating sentiment tuples as paths of a tree[C]//Findings of the Association for Computational Linguistics: ACL 2022. Stroudsburg: ACL, 2022: 2215-2225.
[58] ZHENG G M, WANG J, YU L C, et al. Instruction tuning with retrieval-based examples ranking for aspect-based sentiment analysis[C]//Findings of the Association for Computational Linguistics ACL 2024. Stroudsburg: ACL, 2024: 4777-4788.
[59] JU X C, ZHANG D, XIAO R, et al. Joint multi-modal aspect-sentiment analysis with auxiliary cross-modal relation detection[C]//Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing. Stroudsburg: ACL, 2021: 4395-4405.
[60] WEI Y W, YUAN S Z, YANG R S, et al. Tackling modality heterogeneity with multi-view calibration network for multimodal sentiment detection[C]//Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics. Stroudsburg: ACL, 2023: 5240-5252.
[61] CHAUDHARI P, KUMAR A, RAGHAW S, et al. GCM-Net: graph-enhanced cross-modal infusion with a metaheuristic-driven network for video sentiment and emotion analysis[J]. arXiv:2410.12828, 2024.
[62] LI Y, ZHU Q M, HE H, et al. MOC: multi-modal sentiment analysis via optimal transport and contrastive interactions[C]//Proceedings of the 30th International Conference on Neural Information Processing. Singapore: Springer, 2024: 439-451.
[63] LING Y, YU J F, XIA R. Vision-language pre-training for multimodal aspect-based sentiment analysis[C]//Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics. Stroudsburg: ACL, 2022: 2149-2159.
[64] ZHOU R, GUO W Y, LIU X M, et al. AoM: detecting aspect-oriented information for multimodal aspect-based sentiment analysis[C]//Findings of the Association for Computational Linguistics: ACL 2023. Stroudsburg: ACL, 2023: 8184-8196.
[65] LI Y, LAN X Y, CHEN H F, et al. Multimodal PEAR chain-of-thought reasoning for multimodal sentiment analysis[J]. ACM Transactions on Multimedia Computing, Communications, and Applications, 2024, 20(9): 1-23.
[66] LUO M, FEI H, LI B B, et al. PanoSent: a panoptic sextuple extraction benchmark for multimodal conversational aspect-based sentiment analysis[C]//Proceedings of the 32nd ACM International Conference on Multimedia. New York: ACM, 2024: 7667-7676.
[67] YANG H, ZHAO Y Y, QIN B. Face-sensitive image-to-emotional-text cross-modal translation for multimodal aspect-based sentiment analysis[C]//Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing. Stroudsburg: ACL, 2022: 3324-3335.
[68] YANG X C, FENG S, WANG D L, et al. Few-shot joint multimodal aspect-sentiment analysis based on generative multimodal prompt[C]//Findings of the Association for Computational Linguistics: ACL 2023. Stroudsburg: ACL, 2023: 11575-11589.
[69] QIAN F, HAN J Q, HE Y J, et al. Sentiment knowledge enhanced self-supervised learning for multimodal sentiment analysis[C]//Findings of the Association for Computational Linguistics: ACL 2023. Stroudsburg: ACL, 2023: 12966-12978.
[70] GUO Z R, JIN T, ZHAO Z. Multimodal prompt learning with missing modalities for sentiment analysis and emotion recognition[C]//Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics. Stroudsburg: ACL, 2024: 1726-1736.
[71] RANA T A, CHEAH Y N, RANA T. Multi-level knowledge-based approach for implicit aspect identification[J]. Applied Intelligence, 2020, 50(12): 4616-4630.
[72] MIR J, MAHMOOD A, KHATOON S. Multi-level knowledge engineering approach for mapping implicit aspects to explicit aspects[J]. Computers, Materials & Continua, 2022, 70(2): 3491-3509.
[73] LI Z Y, ZOU Y C, ZHANG C, et al. Learning implicit sentiment in aspect-based sentiment analysis with supervised contrastive pre-training[C]//Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing. Stroudsburg: ACL, 2021: 246-256.
[74] BENARAFA H, BENKHALIFA M, AKHLOUFI M. An improved SVM noise tolerance for implicit aspect identification in sentiment analysis[J]. Journal of Advances in Information Technology, 2024, 15(7): 838-852.
[75] 杨春霞, 韩煜, 陈启岗, 等. 基于BERT与注意力机制的方面级隐式情感分析模型[J]. 南京信息工程大学学报 (自然科学版), 2023, 15(5): 551-560.
YANG C X, HAN Y, CHEN Q G, et al. Aspect-based implicit sentiment analysis model based on BERT and attention mechanism[J]. Journal of Nanjing University of Information Science & Technology (Natural Science Edition), 2023, 15(5): 551-560.
[76] CUI J, FUKUMOTO F, WANG X F, et al. Aspect-category enhanced learning with a neural coherence model for implicit sentiment analysis[C]//Findings of the Association for Computational Linguistics: EMNLP 2023. Stroudsburg: ACL, 2023: 11345-11358.
[77] FEI H, LI B B, LIU Q, et al. Reasoning implicit sentiment with chain-of-thought prompting[C]//Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics. Stroudsburg: ACL, 2023: 1171-1182.
[78] ZHANG H, CHEAH Y N, HE C Q, et al. An instruction tuning-based contrastive learning framework for aspect sentiment quad prediction with implicit aspects and opinions[C]//Findings of the Association for Computational Linguistics: EMNLP 2024. Stroudsburg: ACL, 2024: 7698-7714.
[79] WANG X L, LI X, YIN Y H, et al. Implicit aspect-based generative model for sentiment analysis based on prompt learning[C]//Proceedings of the 2024 5th International Conference on Big Data & Artificial Intelligence & Software Engineering. Piscataway: IEEE, 2024: 94-97.
[80] PAN S J, YANG Q. A survey on transfer learning[J]. IEEE Transactions on Knowledge and Data Engineering, 2009, 22(10): 1345-1359.
[81] JAKOB N, GUREVYCH I. Extracting opinion targets in a single and cross-domain setting with conditional random fields[C]//Proceedings of the 2010 Conference on Empirical Methods in Natural Language Processing, 2010: 1035-1045.
[82] WANG W Y, PAN S J. Syntactically meaningful and transferable recursive neural networks for aspect and opinion extraction[J]. Computational Linguistics, 2020, 45(4): 705-736.
[83] ZENG Y S, WANG G H, REN H P, et al. Enhance cross-domain aspect-based sentiment analysis by incorporating commonsense relational structure[J]. Proceedings of the AAAI Conference on Artificial Intelligence, 2022, 36(11): 13105-13106.
[84] WANG Q L, WEN Z Y, DING K Y, et al. Cross-domain sentiment analysis via disentangled representation and prototypical learning[J]. IEEE Transactions on Affective Computing, 2025, 16(1): 264-276.
[85] XU H, LIU B, SHU L, et al. DomBERT: domain-oriented language model for aspect-based sentiment analysis[C]//Findings of the Association for Computational Linguistics: EMNLP 2020. Stroudsburg: ACL, 2020: 1725-1731.
[86] YU J F, ZHAO Q K, XIA R. Cross-domain data augmentation with domain-adaptive language modeling for aspect-based sentiment analysis[C]//Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics. Stroudsburg: ACL, 2023: 1456-1470.
[87] WANG S, ZHOU J, CHEN Q, et al. Domain generalization via causal adjustment for cross-domain sentiment analysis[C]//Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation, 2024: 5286-5298.
[88] LUO H, LI L K, FANG L N, et al. Domain adaptation for object classification in point clouds via asymmetrical Siamese and conditional adversarial network[J]. IEEE Geoscience and Remote Sensing Letters, 2022, 19: 7004605.
[89] PONTIKI M, GALANIS D, PAVLOPOULOS J, et al. SemEval-2014 task 4: aspect based sentiment analysis[C]//Proceedings of the 8th International Workshop on Semantic Evaluation. Stroudsburg: ACL, 2014: 27-35.
[90] PONTIKI M, GALANIS D, PAPAGEORGIOU H, et al. SemEval-2015 task 12: aspect based sentiment analysis[C]//Proceedings of the 9th International Workshop on Semantic Evaluation. Stroudsburg: ACL, 2015: 486-495.
[91] PONTIKI M, GALANIS D, PAPAGEORGIOU H, et al. SemEval-2016 task 5: aspect based sentiment analysis[C]//Proceedings of the 10th International Workshop on Semantic Evaluation. Stroudsburg: ACL, 2016: 19-30.
[92] DONG L, WEI F R, TAN C Q, et al. Adaptive recursive neural network for target-dependent Twitter sentiment classification[C]//Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics. Stroudsburg: ACL, 2014: 49-54.
[93] JIANG Q N, CHEN L, XU R F, et al. A challenge dataset and effective models for aspect-based sentiment analysis[C]//Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing. Stroudsburg: ACL, 2019: 6279-6284.
[94] CAI H J, XIA R, YU J F. Aspect-category-opinion-sentiment quadruple extraction with implicit aspects and opinions[C]//Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing. Stroudsburg: ACL, 2021: 340-350.
[95] ZADEH A, LIANG P P, VANBRIESEN J, et al. Multimodal language analysis in the wild: CMU-MOSEI dataset and interpretable dynamic fusion graph[C]//Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics. Stroudsburg: ACL, 2018: 2236-2246.
[96] ZHOU Y H, TU F J, SHA K W, et al. A survey on data quality dimensions and tools for machine learning[C]//Proceedings of the 2024 IEEE International Conference on Artificial Intelligence Testing. Piscataway: IEEE, 2024: 120-131.