{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,9,11]],"date-time":"2025-09-11T19:59:41Z","timestamp":1757620781863,"version":"3.44.0"},"reference-count":71,"publisher":"Springer Science and Business Media LLC","issue":"9","license":[{"start":{"date-parts":[[2025,7,31]],"date-time":"2025-07-31T00:00:00Z","timestamp":1753920000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"},{"start":{"date-parts":[[2025,7,31]],"date-time":"2025-07-31T00:00:00Z","timestamp":1753920000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"crossref","award":["62107032"],"award-info":[{"award-number":["62107032"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62377027"],"award-info":[{"award-number":["62377027"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Complex Intell. Syst."],"published-print":{"date-parts":[[2025,9]]},"DOI":"10.1007\/s40747-025-02034-0","type":"journal-article","created":{"date-parts":[[2025,7,31]],"date-time":"2025-07-31T10:39:46Z","timestamp":1753958386000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Enhancing aspect-based sentiment analysis with multiple-knowledge promotion and multi-perspective noise filtering"],"prefix":"10.1007","volume":"11","author":[{"given":"Juan","family":"Yang","sequence":"first","affiliation":[]},{"given":"Yali","family":"Xiao","sequence":"additional","affiliation":[]},{"given":"Xu","family":"Du","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2025,7,31]]},"reference":[{"key":"2034_CR1","doi-asserted-by":"publisher","first-page":"289","DOI":"10.1016\/j.inffus.2022.12.004","volume":"92","author":"HY Wu","year":"2023","unstructured":"Wu HY, Huang CG, Deng SC (2023) Improving aspect-based sentiment analysis with knowledge-aware dependency graph network. Inf Fusion 92:289\u2013299","journal-title":"Inf Fusion"},{"key":"2034_CR2","doi-asserted-by":"crossref","unstructured":"Yang L, Yu J, Zhang C, Na J (2021) Fine-grained sentiment analysis of political tweets with entity-aware multimodal network, diversity, divergence. In: Proceedings of the 16th international conference, pp 411\u2013420","DOI":"10.1007\/978-3-030-71292-1_31"},{"key":"2034_CR3","doi-asserted-by":"crossref","unstructured":"Hu M, Liu B (2004) Mining and summarizing customer reviews. In: Proceedings of the 10th ACM SIGKDD international conference on knowledge discovery and data mining, pp 168\u2013177","DOI":"10.1145\/1014052.1014073"},{"key":"2034_CR4","doi-asserted-by":"publisher","DOI":"10.1016\/j.engappai.2024.108131","volume":"133","author":"MS Aslam","year":"2024","unstructured":"Aslam MS, Bilal H, Band SS, Ghasemi P (2024) Modeling of nonlinear supply chain management with lead-times based on Takagi-Sugeno fuzzy control model. Eng Appl Artif Intell 133:108131","journal-title":"Eng Appl Artif Intell"},{"key":"2034_CR5","doi-asserted-by":"publisher","DOI":"10.1007\/s00779-021-01595-4","author":"G Hanane","year":"2021","unstructured":"Hanane G, Habib NE (2021) Deep associative learning approach for bio-medical sentiment analysis utilizing unsupervised representation from large-scale patients\u2019 narratives. Pers Ubiquitous Comput. https:\/\/doi.org\/10.1007\/s00779-021-01595-4","journal-title":"Pers Ubiquitous Comput"},{"issue":"19","key":"2034_CR6","doi-asserted-by":"publisher","first-page":"31422","DOI":"10.1109\/JIOT.2024.3418352","volume":"11","author":"H Bilal","year":"2024","unstructured":"Bilal H, Obaidat MS, Aslam MS, Zhang J, Yin BQ, Mahmood K (2024) Online fault diagnosis of industrial robot using IoRT and hybrid deep learning techniques: an experimental approach. IEEE Internet Things J 11(19):31422\u201331437","journal-title":"IEEE Internet Things J"},{"key":"2034_CR7","first-page":"71","volume":"14","author":"H Bilal","year":"2024","unstructured":"Bilal H, Ahmed F, Aslam MS, Li QM, Hou J, Yin BQ (2024) A blockchain-enabled approach for privacy-protected data sharing in internet of robotic things networks. Human-Centric Comput Inf Sci 14:71","journal-title":"Human-Centric Comput Inf Sci"},{"key":"2034_CR8","doi-asserted-by":"publisher","first-page":"150574","DOI":"10.1109\/ACCESS.2024.3453944","volume":"12","author":"H Bilal","year":"2024","unstructured":"Bilal H, Aslam MS, Tian YB, Yahya A, Abu lzneid B (2024) Enhancing trajectory tracking and vibration control of flexible robots with hybrid fuzzy ADRC and input shaping. IEEE Access 12:150574\u2013150591","journal-title":"IEEE Access"},{"key":"2034_CR9","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2021.106810","volume":"217","author":"M Liu","year":"2021","unstructured":"Liu M, Zhou F, Chen K, Zhao Y (2021) Co-attention networks based on aspect and context for aspect-level sentiment analysis. Knowl-Based Syst 217:106810","journal-title":"Knowl-Based Syst"},{"issue":"2","key":"2034_CR10","doi-asserted-by":"publisher","first-page":"308","DOI":"10.1016\/j.ipm.2018.01.006","volume":"56","author":"M Al-Smadi","year":"2019","unstructured":"Al-Smadi M, Al-Ayyoub M, Jararweh Y, Qawasmeh O (2019) Enhancing aspect-based sentiment analysis of Arabic hotels\u2019 reviews using morphological, syntactic and semantic features. Inf Process Manag 56(2):308\u2013319","journal-title":"Inf Process Manag"},{"key":"2034_CR11","doi-asserted-by":"crossref","unstructured":"Chakraborty S, Goyal P, Mukherjee A (2020) Aspect-based sentiment analysis of scientific reviews. In: Proceedings of the ACM\/IEEE joint conference on digital libraries, pp 207\u2013216","DOI":"10.1145\/3383583.3398541"},{"key":"2034_CR12","doi-asserted-by":"crossref","unstructured":"Xue W, Li T (2018) Aspect-based sentiment analysis with gated convolutional networks. In: Proceedings of the 56th annual meeting of the association for computational linguistics, pp 2514\u20132523","DOI":"10.18653\/v1\/P18-1234"},{"issue":"2","key":"2034_CR13","first-page":"318","volume":"58","author":"J Yang","year":"2024","unstructured":"Yang J, Li ZK, Du X (2024) Analyzing audiovisual data for understanding user\u2019s emotion in human\u2212computer interaction environment. Data Technol Appl 58(2):318\u2013343","journal-title":"Data Technol Appl"},{"key":"2034_CR14","doi-asserted-by":"publisher","first-page":"121","DOI":"10.1007\/s00429-007-0154-0","volume":"212","author":"C Kayser","year":"2007","unstructured":"Kayser C, Logothetis NK (2007) Do early sensory cortices integrate cross-modal information? Brain Struct Funct 212:121\u2013132","journal-title":"Brain Struct Funct"},{"key":"2034_CR15","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2023.127222","volume":"573","author":"J Yang","year":"2024","unstructured":"Yang J, Xu MY, Xiao YL, Du X (2024) AMIFN: aspect-guided multi-view interactions and fusion network for multimodal aspect-based sentiment analysis. Neurocomputing 573:127222","journal-title":"Neurocomputing"},{"key":"2034_CR16","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2024.111724","volume":"293","author":"J Yang","year":"2024","unstructured":"Yang J, Xiao YL, Du X (2024) Multi-grained fusion network with self-distillation for aspect-based multimodal sentiment analysis. Knowl-Based Syst 293:111724","journal-title":"Knowl-Based Syst"},{"issue":"3","key":"2034_CR17","doi-asserted-by":"publisher","first-page":"1966","DOI":"10.1109\/TAFFC.2022.3171091","volume":"14","author":"J Yu","year":"2023","unstructured":"Yu J, Chen K, Xia R (2023) Hierarchical interactive multimodal transformer for aspect-based multimodal sentiment analysis. IEEE Trans Affect Comput 14(3):1966\u20131978","journal-title":"IEEE Trans Affect Comput"},{"key":"2034_CR18","doi-asserted-by":"publisher","first-page":"429","DOI":"10.1109\/TASLP.2019.2957872","volume":"28","author":"J Yu","year":"2020","unstructured":"Yu J, Jiang J, Xia R (2020) Entity-sensitive attention and fusion network for entity-level multimodal sentiment classification. IEEE ACM Trans Audio Speech Lang Process 28:429\u2013439","journal-title":"IEEE ACM Trans Audio Speech Lang Process"},{"key":"2034_CR19","doi-asserted-by":"crossref","unstructured":"Yu J, Jiang J (2019) Adapting BERT for target-oriented multimodal sentiment classification. In: Proceedings of the 26th international joint conference on artificial intelligence, pp 5408\u20135414","DOI":"10.24963\/ijcai.2019\/751"},{"key":"2034_CR20","unstructured":"Tang D, Qin B, Feng X, Liu T (2016) Effective LSTMs for target-dependent sentiment classification. In: Proceedings of the 26th international conference on computational linguistics, pp 3298\u20133307"},{"key":"2034_CR21","doi-asserted-by":"crossref","unstructured":"Wang Y, Huang M, Zhu X, Zhao L (2016) Attention-based LSTM for aspect-level sentiment classification. In: Proceedings of the conference on empirical methods in natural language processing, pp 606\u2013615","DOI":"10.18653\/v1\/D16-1058"},{"key":"2034_CR22","unstructured":"Vaswani A, Shazeer N, Parmar N, Uszkoreit J, Jones L, Gomez AN et al (2017) Attention is all you need. In: Proceedings of the 31st conference on neural information processing systems, pp 1\u201315"},{"issue":"3","key":"2034_CR23","first-page":"5801","volume":"75","author":"JY An","year":"2023","unstructured":"An JY, Zainon WMNW, Hao Z (2023) Improving targeted multimodal sentiment classification with semantic description of images. CMC Comput Mater Continua 75(3):5801\u20135815","journal-title":"CMC Comput Mater Continua"},{"key":"2034_CR24","doi-asserted-by":"crossref","unstructured":"Hou X, Huang J, Wang G, Qi P, He X, Zhou B (2021) Selective attention based graph convolutional networks for aspect-level sentiment classification. In: Proceedings of the ACM 15th workshop graph-based methods natural language processing, pp 83\u201393","DOI":"10.18653\/v1\/11.textgraphs-1.8"},{"key":"2034_CR25","doi-asserted-by":"crossref","unstructured":"Khan Z, Fu Y (2021) Exploiting BERT for multimodal target sentiment classification through input space translation. In: Proceedings of the 29th ACM international conference on multimedia, association for computing machinery, pp 3034\u20133042","DOI":"10.1145\/3474085.3475692"},{"key":"2034_CR26","unstructured":"Zhao F, Wu Z, Long S, Dai X, Huang S, Chen J (2022) Learning from adjective-noun pairs: a knowledge-enhanced framework for target-oriented multimodal sentiment classification. In: Proceedings of the 29th international conference on computational linguistics, pp 6784\u20136794"},{"key":"2034_CR27","doi-asserted-by":"crossref","unstructured":"Yu J, Wang J, Xia R, Li J (2022) Targeted multimodal sentiment classification based on coarse-to-fine grained image-target matching. In: Proceedings of the thirty-first international joint conference on artificial intelligence, pp 4482\u20134488","DOI":"10.24963\/ijcai.2022\/622"},{"key":"2034_CR28","doi-asserted-by":"publisher","DOI":"10.1016\/j.jksuci.2023.101791","volume":"35","author":"J Yang","year":"2023","unstructured":"Yang J, Dong XX, Du X (2023) SMFNM: semi-supervised multimodal fusion network with main-modal for real-time emotion recognition in conversations. J King Saud Univ Comput Inf Sci 35:101791","journal-title":"J King Saud Univ Comput Inf Sci"},{"issue":"2","key":"2034_CR29","doi-asserted-by":"publisher","first-page":"450","DOI":"10.1109\/TCSS.2019.2956957","volume":"7","author":"K Chakraborty","year":"2020","unstructured":"Chakraborty K, Bhattacharyya S, Bag R (2020) A survey of sentiment analysis from social media data. IEEE Trans Comput Soc Syst 7(2):450\u2013464","journal-title":"IEEE Trans Comput Soc Syst"},{"issue":"6","key":"2034_CR30","doi-asserted-by":"publisher","first-page":"1358","DOI":"10.1109\/TCSS.2020.3033302","volume":"7","author":"HY Liu","year":"2020","unstructured":"Liu HY, Chatterjee I, Zhou MC, Lu XYS, Abusorrah A (2020) Aspect-based sentiment analysis: a survey of deep learning methods. IEEE Trans Comput Soc Syst 7(6):1358\u20131375","journal-title":"IEEE Trans Comput Soc Syst"},{"key":"2034_CR31","doi-asserted-by":"publisher","DOI":"10.1016\/j.asoc.2023.111206","volume":"152","author":"A Pandey","year":"2024","unstructured":"Pandey A, Vishwakarma DK (2024) Progress, achievements, and challenges in multimodal sentiment analysis using deep learning: a survey. Appl Soft Comput 152:111206","journal-title":"Appl Soft Comput"},{"key":"2034_CR32","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2022.109259","volume":"136","author":"D Wang","year":"2023","unstructured":"Wang D, Guo XT, Tian YM, Liu JH, He LH, Luo XM (2023) TETFN: a text enhanced transformer fusion network for multimodal sentiment analysis. Pattern Recogn 136:109259","journal-title":"Pattern Recogn"},{"key":"2034_CR33","doi-asserted-by":"publisher","first-page":"2689","DOI":"10.1109\/TASLP.2022.3192728","volume":"30","author":"QP Chen","year":"2022","unstructured":"Chen QP, Huang GM, Wang YB (2022) The weighted cross-modal attention mechanism with sentiment prediction auxiliary task for multimodal sentiment analysis. IEEE Trans Audio Speech Lang Process 30:2689\u20132695","journal-title":"IEEE Trans Audio Speech Lang Process"},{"key":"2034_CR34","doi-asserted-by":"crossref","unstructured":"Zadeh A, Liang PP, Poria S, Vij P, Cambria E, Morency L (2018) Multi-attention recurrent network for human communication comprehension. In: Proceedings of the thirty-second AAAI conference on artificial intelligence (AAAI-18), pp 5642\u20135649","DOI":"10.1609\/aaai.v32i1.12024"},{"key":"2034_CR35","doi-asserted-by":"crossref","unstructured":"Hu A, Flaxman SR (2018) Multimodal sentiment analysis to explore the structure of emotions. In: Proceedings of the ACM SIGKDD international conference on knowledge discovery and data mining, pp 350\u2013358","DOI":"10.1145\/3219819.3219853"},{"key":"2034_CR36","doi-asserted-by":"crossref","unstructured":"Wang H, Meghawat A, Morency L, Xing EP (2017) Select-additive learning: Improving generalization in multimodal sentiment analysis. In: Proceedings of the 2017 IEEE international conference on multimedia and expo, pp 949\u2013954","DOI":"10.1109\/ICME.2017.8019301"},{"key":"2034_CR37","doi-asserted-by":"crossref","unstructured":"Kumar A, Vepa J (2020) Gated mechanism for attention based multi modal sentiment analysis. In: Proceedings of the IEEE international conference on acoustics, speech, and signal processing, pp 4477\u20134481","DOI":"10.1109\/ICASSP40776.2020.9053012"},{"key":"2034_CR38","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1109\/TIP.2019.2923608","volume":"29","author":"Y Wei","year":"2020","unstructured":"Wei Y, Wang X, Guan W, Nie L, Lin Z, Chen B (2020) Neural multimodal cooperative learning toward micro-video understanding. IEEE Trans Image Process 29:1\u201314","journal-title":"IEEE Trans Image Process"},{"key":"2034_CR39","unstructured":"Liu Y, Ott M, Goyal N, Du J, Joshi M, Chen D et al (2019) Roberta: a robustly optimized bert pretraining approach. arXiv:1907.11692"},{"key":"2034_CR40","doi-asserted-by":"crossref","unstructured":"He K, Zhang X, Ren S, and Sun J (2016) Deep residual learning for image recognition. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 770\u2013778","DOI":"10.1109\/CVPR.2016.90"},{"key":"2034_CR41","doi-asserted-by":"crossref","unstructured":"Szegedy C, Liu W, Jia YQ, Sermanet P, Reed SE, Anguelov D (2015) Going deeper with convolutions. In: Proceedings of the IEEE conference on computing vision and pattern recognition, pp 1\u20139","DOI":"10.1109\/CVPR.2015.7298594"},{"key":"2034_CR42","doi-asserted-by":"crossref","unstructured":"Kampman O, Barezi EJ, Bertero D, Fung P (2018) Investigating audio, visual, and text fusion methods for end-to-end automatic personality prediction. arXiv:1805.00705 [CoRR abs]","DOI":"10.18653\/v1\/P18-2096"},{"key":"2034_CR43","doi-asserted-by":"crossref","unstructured":"Mai S, Hu H, Xing S (2020) Modality to modality translation: an adversarial representation learning and graph fusion network for multimodal fusion. In: Proceedings of the 34th AAAI conference on artificial intelligence, pp 164\u2013172","DOI":"10.1609\/aaai.v34i01.5347"},{"issue":"12","key":"2034_CR44","doi-asserted-by":"publisher","first-page":"16138","DOI":"10.1007\/s10489-022-04307-4","volume":"53","author":"ZG Zhao","year":"2020","unstructured":"Zhao ZG, Tang MW, Zhao FJ, Zhang ZH, Chen XL (2020) Incorporating semantics, syntax and knowledge for aspect-based sentiment analysis. Appl Intell 53(12):16138\u201316150","journal-title":"Appl Intell"},{"key":"2034_CR45","doi-asserted-by":"publisher","DOI":"10.1016\/j.ipm.2023.103508","volume":"60","author":"LW Xiao","year":"2023","unstructured":"Xiao LW, Wu XJ, Yang SW, Xu JJ, Zhou J, He L (2023) Cross-modal fine-grained alignment and fusion network for multimodal aspect-based sentiment analysis. Inf Process Manag 60:103508","journal-title":"Inf Process Manag"},{"key":"2034_CR46","doi-asserted-by":"publisher","first-page":"149","DOI":"10.1016\/j.inffus.2022.10.004","volume":"91","author":"HT Phan","year":"2023","unstructured":"Phan HT, Nguyen NT, Hwang D (2023) Aspect-level sentiment analysis: a survey of graph convolutional network methods. Inf Fusion 91:149\u2013172","journal-title":"Inf Fusion"},{"key":"2034_CR47","unstructured":"Tishby N, Pereira FC, Bialek W (1999) The information bottleneck method. In: Proceedings of the 37th annual allerton conference on communication, control, and computing"},{"key":"2034_CR48","doi-asserted-by":"crossref","unstructured":"Tishby N, Zaslavsky N (2015) Deep learning and the information bottleneck principle. In: Proceedings of the 2015 IEEE information theory workshop, pp 1\u20135","DOI":"10.1109\/ITW.2015.7133169"},{"key":"2034_CR49","unstructured":"Alemi A, Fischer I, Dillon JV, Murphy K (2017) Deep variational information bottleneck. In: Proceedings of the international conference on learning representations"},{"key":"2034_CR50","doi-asserted-by":"crossref","unstructured":"Wan Z, Zhang C, Zhu P, Hu Q (2021) Multi-view information-bottleneck representation learning. In: Proceedings of AAAI conference on artificial intelligence, pp 10085\u201310092","DOI":"10.1609\/aaai.v35i11.17210"},{"key":"2034_CR51","doi-asserted-by":"publisher","first-page":"24","DOI":"10.1109\/TMM.2021.3120537","volume":"25","author":"JY Xie","year":"2021","unstructured":"Xie JY, Zhu YC, Chen ZZ (2021) Micro-video popularity prediction via multimodal variational information bottleneck. IEEE Trans Multimed 25:24\u201337","journal-title":"IEEE Trans Multimed"},{"key":"2034_CR52","doi-asserted-by":"publisher","first-page":"111551","DOI":"10.1016\/j.knosys.2024.111551","volume":"289","author":"J Hu","year":"2024","unstructured":"Hu J, Yang CH, Huang K, Wang HJ, Peng B, Li TR (2024) Information bottleneck fusion for deep multi-view clustering. Knowl-Based Syst 289:111551","journal-title":"Knowl-Based Syst"},{"issue":"6","key":"2034_CR53","doi-asserted-by":"publisher","first-page":"1137","DOI":"10.1109\/TPAMI.2016.2577031","volume":"39","author":"SQ Ren","year":"2017","unstructured":"Ren SQ, He KM, Girshick R, Sun J (2017) Faster R-CNN: towards real-time object detection with region. IEEE Trans Pattern Anal Mach Intell 39(6):1137\u20131149","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"2034_CR54","unstructured":"Dosovitskiy A, Beyer L, Kolesnikov A, Weissenborn D, Zhai XH, Unterthiner T et al (2021) An image is worth 16 \u00d7 16 words: transformer for image recognition at scale. In: Proceedings of the international conference on learning representations"},{"key":"2034_CR55","doi-asserted-by":"crossref","unstructured":"Ma YK, Peng HY, Cambria E (2018) Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive lstm. In: Proceedings of the AAAI conference on artificial intelligence, pp 5876\u20135883","DOI":"10.1609\/aaai.v32i1.12048"},{"key":"2034_CR56","doi-asserted-by":"crossref","unstructured":"Nguyen DQ, Vu T, Nguyen AT (2020) Bertweet: a pretrained language model for english tweets. In: Proceedings of EMNLP, pp 9\u201314","DOI":"10.18653\/v1\/2020.emnlp-demos.2"},{"key":"2034_CR57","doi-asserted-by":"crossref","unstructured":"Lin TY, Maire M, Belongie S, Hays J, Perona P, Ramanan D et al. (2014) Microsoft coco: common objects in context. In: Proceedings of ECCV, pp 740\u2013755","DOI":"10.1007\/978-3-319-10602-1_48"},{"key":"2034_CR58","doi-asserted-by":"publisher","unstructured":"Kingma DP, Ba J (2015) Adam: a method for stochastic optimization. In: Proceedings of the 3rd international conference on learning representations. https:\/\/doi.org\/10.48550\/arXiv.1412.6980.","DOI":"10.48550\/arXiv.1412.6980"},{"key":"2034_CR59","unstructured":"Devlin J, Chang MW, Lee K, Toutanova K (2019) Bert: pre-training of deep bidirectional transformers for language understanding. In: Proceedings of the 2019 conference of the North American chapter of the association for computational linguistics, pp 4171\u20134186"},{"key":"2034_CR60","doi-asserted-by":"crossref","unstructured":"Fan F, Feng Y, Zhao D (2018) Multi-grained attention network for aspect-level sentiment classification. In: Proceedings of the 2018 conference on empirical methods in natural language processing, pp 3433\u20133442","DOI":"10.18653\/v1\/D18-1380"},{"key":"2034_CR61","doi-asserted-by":"crossref","unstructured":"Ma D, Li S, Zhang X, Wang H (2017) Interactive attention networks for aspect-level sentiment classification. In: Proceedings of the twenty-sixth international joint conference on artificial intelligence, pp 4068\u20134074","DOI":"10.24963\/ijcai.2017\/568"},{"key":"2034_CR62","doi-asserted-by":"crossref","unstructured":"Chen P, Sun Z, Bing L, Yang W (2017) Recurrent attention network on memory for aspect sentiment analysis. In: Proceedings of the 2017 conference on empirical methods in natural language processing, pp 452\u2013461","DOI":"10.18653\/v1\/D17-1047"},{"key":"2034_CR63","unstructured":"Sun C, Huang L, Qiu X (2019) Utilizing bert for aspect-based sentiment analysis via constructing auxiliary sentence. In: Proceedings of NAACL, pp 380\u2013385"},{"key":"2034_CR64","doi-asserted-by":"publisher","DOI":"10.1016\/j.artint.2021.103477","volume":"296","author":"J Su","year":"2021","unstructured":"Su J, Tang J, Jiang H, Lu Z, Ge Y, Song L et al (2021) Enhanced aspect-based sentiment analysis models with progressive self-supervised attention learning. Artif Intell 296:103477","journal-title":"Artif Intell"},{"key":"2034_CR65","doi-asserted-by":"crossref","unstructured":"Xu N, Mao W, Chen G (2019) Multi-interactive memory network for aspect based multimodal sentiment analysis. In: Proceedings of the AAAI conference on artificial intelligence, pp 371\u201337","DOI":"10.1609\/aaai.v33i01.3301371"},{"key":"2034_CR66","unstructured":"Lu J, Batra D, Parikh D, Lee S (2019) Vilbert: pretraining task-agnostic visiolinguistic representations for vision-and-language tasks. In: Proceedings of neural information processing systems, pp 13\u201323"},{"key":"2034_CR67","doi-asserted-by":"crossref","unstructured":"Yang H, Zhao Y, Qin B (2022) Face-sensitive image-to-emotional-text cross-modal translation for multimodal aspect-based sentiment analysis. In: Proceedings of the 2022 conference on empirical methods in natural language processing, pp 3324\u20133335","DOI":"10.18653\/v1\/2022.emnlp-main.219"},{"key":"2034_CR68","doi-asserted-by":"crossref","unstructured":"Yang D, Li XH, Li Z, Zhou CY, Wang XF, Chen F (2024) Prompt fusion interaction transformer for aspect-based multimodal sentiment analysis. In: Proceedings of the 2024 IEEE international conference on multimedia and expo, pp 1\u20136","DOI":"10.1109\/ICME57554.2024.10687885"},{"key":"2034_CR69","doi-asserted-by":"publisher","DOI":"10.7717\/peerj-cs.1904","volume":"10","author":"TJ Zhang","year":"2024","unstructured":"Zhang TJ, Zhou G, Lu JC, Li ZB, Wu H, Liu S (2024) Text-image semantic relevance identification for aspect-based multimodal sentiment analysis. PeerJ Comput Sci 10:e1904","journal-title":"PeerJ Comput Sci"},{"issue":"1","key":"2034_CR70","doi-asserted-by":"publisher","first-page":"1","DOI":"10.4018\/IJSWIS.337598","volume":"20","author":"J Yang","year":"2024","unstructured":"Yang J, Xiong YJ (2024) Bidirectional complementary correlation-based multimodal aspect-level sentiment analysis. Int J Semant Web Inf Syst 20(1):1\u201316","journal-title":"Int J Semant Web Inf Syst"},{"issue":"1","key":"2034_CR71","doi-asserted-by":"publisher","first-page":"46","DOI":"10.1007\/s11227-024-06567-y","volume":"81","author":"YZ Chen","year":"2025","unstructured":"Chen YZ, Shi LY, Lin JL, Chen JT, Zhong JY, Dong C (2025) Multi-granularity visual-textual jointly modeling for aspect-level multimodal sentiment analysis. J Supercomput 81(1):46","journal-title":"J Supercomput"}],"container-title":["Complex &amp; Intelligent Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s40747-025-02034-0.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s40747-025-02034-0\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s40747-025-02034-0.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,9,8]],"date-time":"2025-09-08T10:36:18Z","timestamp":1757327778000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s40747-025-02034-0"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,7,31]]},"references-count":71,"journal-issue":{"issue":"9","published-print":{"date-parts":[[2025,9]]}},"alternative-id":["2034"],"URL":"https:\/\/doi.org\/10.1007\/s40747-025-02034-0","relation":{},"ISSN":["2199-4536","2198-6053"],"issn-type":[{"type":"print","value":"2199-4536"},{"type":"electronic","value":"2198-6053"}],"subject":[],"published":{"date-parts":[[2025,7,31]]},"assertion":[{"value":"4 November 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"15 July 2025","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"31 July 2025","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}],"article-number":"404"}}