{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,23]],"date-time":"2026-01-23T15:34:52Z","timestamp":1769182492770,"version":"3.49.0"},"reference-count":39,"publisher":"Springer Science and Business Media LLC","issue":"2","license":[{"start":{"date-parts":[[2024,8,30]],"date-time":"2024-08-30T00:00:00Z","timestamp":1724976000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,8,30]],"date-time":"2024-08-30T00:00:00Z","timestamp":1724976000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"DOI":"10.13039\/501100021171","name":"Guangdong Basic and Applied Basic Research Foundation","doi-asserted-by":"crossref","award":["2023A1515011370"],"award-info":[{"award-number":["2023A1515011370"]}],"id":[{"id":"10.13039\/501100021171","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"crossref","award":["32371114"],"award-info":[{"award-number":["32371114"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]},{"name":"Characteristic Innovation Projects of Guangdong Colleges and Universities","award":["2018KTSCX049"],"award-info":[{"award-number":["2018KTSCX049"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Int. J. Mach. Learn. & Cyber."],"published-print":{"date-parts":[[2025,2]]},"DOI":"10.1007\/s13042-024-02342-w","type":"journal-article","created":{"date-parts":[[2024,8,30]],"date-time":"2024-08-30T12:02:27Z","timestamp":1725019347000},"page":"1389-1402","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Relevance-aware visual entity filter network for multimodal aspect-based sentiment analysis"],"prefix":"10.1007","volume":"16","author":[{"given":"Yifan","family":"Chen","sequence":"first","affiliation":[]},{"given":"Haoliang","family":"Xiong","sequence":"additional","affiliation":[]},{"given":"Kuntao","family":"Li","sequence":"additional","affiliation":[]},{"given":"Weixing","family":"Mai","sequence":"additional","affiliation":[]},{"given":"Yun","family":"Xue","sequence":"additional","affiliation":[]},{"given":"Qianhua","family":"Cai","sequence":"additional","affiliation":[]},{"given":"Fenghuan","family":"Li","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2024,8,30]]},"reference":[{"key":"2342_CR1","first-page":"1","volume":"01","author":"W Zhang","year":"2022","unstructured":"Zhang W, Li X, Deng Y, Bing L, Lam W (2022) A survey on aspect-based sentiment analysis: tasks, methods, and challenges. IEEE Trans Knowl Data Eng 01:1\u201320","journal-title":"IEEE Trans Knowl Data Eng"},{"issue":"07","key":"2342_CR2","doi-asserted-by":"publisher","first-page":"2357004","DOI":"10.1142\/S0218001423570045","volume":"37","author":"L Jiang","year":"2023","unstructured":"Jiang L, Zou Z, Liao J, Li Y (2023) A study on the application of sentiment-support words on aspect-based sentiment analysis. Int J Pattern Recognit Artif Intell 37(07):2357004","journal-title":"Int J Pattern Recognit Artif Intell"},{"key":"2342_CR3","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2023.110419","volume":"266","author":"L Jiang","year":"2023","unstructured":"Jiang L, Li Y, Liao J, Zou Z, Jiang C (2023) Research on non-dependent aspect-level sentiment analysis. Knowl-Based Syst 266:110419","journal-title":"Knowl-Based Syst"},{"key":"2342_CR4","doi-asserted-by":"crossref","unstructured":"Zhang Z, Zhou Z, Wang Y (2022) Ssegcn: syntactic and semantic enhanced graph convolutional network for aspect-based sentiment analysis. In: Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pp 4916\u20134925","DOI":"10.18653\/v1\/2022.naacl-main.362"},{"key":"2342_CR5","doi-asserted-by":"crossref","unstructured":"Zhang M, Zhu Y, Liu Z, Bao Z, Wu Y, Sun X, Xu L (2023) Span-level aspect-based sentiment analysis via table filling. In: Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp 9273\u20139284","DOI":"10.18653\/v1\/2023.acl-long.515"},{"key":"2342_CR6","doi-asserted-by":"crossref","unstructured":"Wei Y, Yuan S, Yang R, Shen L, Li Z, Wang L, Chen M (2023) Tackling modality heterogeneity with multi-view calibration network for multimodal sentiment detection. In: Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp 5240\u20135252","DOI":"10.18653\/v1\/2023.acl-long.287"},{"key":"2342_CR7","doi-asserted-by":"crossref","unstructured":"Qian F, Han J, He Y, Zheng T, Zheng G (2023) Sentiment knowledge enhanced self-supervised learning for multimodal sentiment analysis. In: Proceedings of the Association for Computational Linguistics: ACL 2023, pp 12966\u201312978","DOI":"10.18653\/v1\/2023.findings-acl.821"},{"key":"2342_CR8","doi-asserted-by":"publisher","first-page":"4909","DOI":"10.1109\/TMM.2022.3183830","volume":"25","author":"D Wang","year":"2022","unstructured":"Wang D, Liu S, Wang Q, Tian Y, He L, Gao X (2022) Cross-modal enhancement network for multimodal sentiment analysis. IEEE Trans Multimed 25:4909\u20134921","journal-title":"IEEE Trans Multimed"},{"key":"2342_CR9","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\u2013378","DOI":"10.1609\/aaai.v33i01.3301371"},{"key":"2342_CR10","doi-asserted-by":"publisher","DOI":"10.1016\/j.inffus.2023.102068","volume":"102","author":"J Gou","year":"2024","unstructured":"Gou J, Xie N, Liu J, Yu B, Ou W, Yi Z, Chen W (2024) Hierarchical graph augmented stacked autoencoders for multi-view representation learning. Inf Fusion 102:102068","journal-title":"Inf Fusion"},{"key":"2342_CR11","doi-asserted-by":"crossref","unstructured":"Yu J, Jiang J (2019) Adapting bert for target-oriented multimodal sentiment classification. In: Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence, pp 5408\u20135414","DOI":"10.24963\/ijcai.2019\/751"},{"issue":"4","key":"2342_CR12","doi-asserted-by":"publisher","first-page":"78","DOI":"10.1007\/s10462-023-10685-z","volume":"57","author":"Y Li","year":"2024","unstructured":"Li Y, Ding H, Lin Y, Feng X, Chang L (2024) Multi-level textual-visual alignment and fusion network for multimodal aspect-based sentiment analysis. Artif Intell Rev 57(4):78","journal-title":"Artif Intell Rev"},{"key":"2342_CR13","doi-asserted-by":"publisher","first-page":"429","DOI":"10.1109\/TASLP.2019.2957872","volume":"28","author":"J Yu","year":"2019","unstructured":"Yu J, Jiang J, Xia R (2019) Entity-sensitive attention and fusion network for entity-level multimodal sentiment classification. IEEE\/ACM Trans Audio Speech Language Process 28:429\u2013439","journal-title":"IEEE\/ACM Trans Audio Speech Language Process"},{"key":"2342_CR14","doi-asserted-by":"publisher","first-page":"1957","DOI":"10.1007\/s11280-021-00955-7","volume":"24","author":"Z Zhang","year":"2021","unstructured":"Zhang Z, Wang Z, Li X, Liu N, Guo B, Yu Z (2021) Modalnet: an aspect-level sentiment classification model by exploring multimodal data with fusion discriminant attentional network. World Wide Web 24:1957\u20131974","journal-title":"World Wide Web"},{"key":"2342_CR15","doi-asserted-by":"crossref","unstructured":"Wang J, Liu Z, Sheng V, Song Y, Qiu C (2021) Saliencybert: recurrent attention network for target-oriented multimodal sentiment classification. In: Proceedings of Chinese Conference on Pattern Recognition and Computer Vision (PRCV), pp 3\u201315","DOI":"10.1007\/978-3-030-88010-1_1"},{"key":"2342_CR16","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, pp 3034\u20133042","DOI":"10.1145\/3474085.3475692"},{"key":"2342_CR17","doi-asserted-by":"crossref","unstructured":"Xiao L, Zhou E, Wu X, Yang S, Ma T, He L (2022) Adaptive multi-feature extraction graph convolutional networks for multimodal target sentiment analysis. In: Proceedings of 2022 IEEE International Conference on Multimedia and Expo (ICME), pp 1\u20136","DOI":"10.1109\/ICME52920.2022.9860020"},{"key":"2342_CR18","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":"2342_CR19","doi-asserted-by":"crossref","unstructured":"Yu J, Chen K, Xia R (2022) Hierarchical interactive multimodal transformer for aspect-based multimodal sentiment analysis. IEEE Trans Affect Comput","DOI":"10.1109\/TAFFC.2022.3171091"},{"key":"2342_CR20","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":"2342_CR21","first-page":"13860","volume":"35","author":"L Sun","year":"2021","unstructured":"Sun L, Wang J, Zhang K, Su Y, Weng F (2021) Rpbert: a text-image relation propagation-based bert model for multimodal ner. Proc AAAI Conf Artif Intell 35:13860\u201313868","journal-title":"Proc AAAI Conf Artif Intell"},{"key":"2342_CR22","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, IJCAI 2022, pp 4482\u20134488","DOI":"10.24963\/ijcai.2022\/622"},{"issue":"5","key":"2342_CR23","doi-asserted-by":"publisher","DOI":"10.1016\/j.ipm.2022.103038","volume":"59","author":"L Yang","year":"2022","unstructured":"Yang L, Na J-C, Yu J (2022) Cross-modal multitask transformer for end-to-end multimodal aspect-based sentiment analysis. Inf Process Manage 59(5):103038","journal-title":"Inf Process Manage"},{"key":"2342_CR24","doi-asserted-by":"crossref","unstructured":"Chen Y, Yuan J, Zhao L, Chen T, Luo R, Davis L, Metaxas DN (2023) More than just attention: improving cross-modal attentions with contrastive constraints for image-text matching. In: Proceedings of the IEEE\/CVF Winter Conference on Applications of Computer Vision, pp 4432\u20134440","DOI":"10.1109\/WACV56688.2023.00441"},{"key":"2342_CR25","doi-asserted-by":"crossref","unstructured":"Pang S, Xue Y, Yan Z, Huang W, Feng J (2021) Dynamic and multi-channel graph convolutional networks for aspect-based sentiment analysis. In: Proceedings of the Association for Computational Linguistics: ACL-IJCNLP 2021, pp 2627\u20132636","DOI":"10.18653\/v1\/2021.findings-acl.232"},{"key":"2342_CR26","doi-asserted-by":"crossref","unstructured":"Li R, Chen H, Feng F, Ma Z, Wang X, Hovy E (2021) Dual graph convolutional networks for aspect-based sentiment analysis. In: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pp 6319\u20136329","DOI":"10.18653\/v1\/2021.acl-long.494"},{"key":"2342_CR27","doi-asserted-by":"crossref","unstructured":"Ju X, Zhang D, Xiao R, Li J, Li S, Zhang M, Zhou G (2021) Joint multi-modal aspect-sentiment analysis with auxiliary cross-modal relation detection. In: Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing, pp 4395\u20134405","DOI":"10.18653\/v1\/2021.emnlp-main.360"},{"key":"2342_CR28","unstructured":"Kenton JDM-WC, Toutanova LK (2019) Bert: pre-training of deep bidirectional transformers for language understanding. In: Proceedings of NAACL-HLT, pp 4171\u20134186"},{"key":"2342_CR29","unstructured":"Kolesnikov A, Dosovitskiy A, Weissenborn D, Heigold G, Uszkoreit J, Beyer L, Minderer M, Dehghani M, Houlsby N, Gelly S et al (2021) An image is worth 16x16 words: transformers for image recognition at scale"},{"key":"2342_CR30","first-page":"21","volume":"1050","author":"JL Ba","year":"2016","unstructured":"Ba JL, Kiros JR, Hinton GE (2016) Layer normalization. Statistics 1050:21","journal-title":"Statistics"},{"key":"2342_CR31","unstructured":"Vaswani A, Shazeer N, Parmar N, Uszkoreit J, Jones L, Gomez AN, Kaiser \u0141, Polosukhin I (2017) Attention is all you need. Adv Neural Inf Process Syst 30"},{"key":"2342_CR32","doi-asserted-by":"crossref","unstructured":"Tsai Y, Bai S, Kolter J, Morency L, Salakhutdinov R (2019) Multimodal transformer for unaligned multimodal language sequences. In: Proceedings of the Conference. Association for Computational Linguistics. Meeting, pp 6558\u20136569","DOI":"10.18653\/v1\/P19-1656"},{"key":"2342_CR33","doi-asserted-by":"crossref","unstructured":"Zhang Q, Fu J, Liu X, Huang X (2018) Adaptive co-attention network for named entity recognition in tweets. In: Proceedings of the AAAI Conference on Artificial Intelligence, pp 5674\u20135681","DOI":"10.1609\/aaai.v32i1.11962"},{"key":"2342_CR34","doi-asserted-by":"crossref","unstructured":"Lu D, Neves L, Carvalho V, Zhang N, Ji H (2018) Visual attention model for name tagging in multimodal social media. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp 1990\u20131999","DOI":"10.18653\/v1\/P18-1185"},{"key":"2342_CR35","doi-asserted-by":"crossref","unstructured":"He K, Zhang X, Ren S, 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"},{"issue":"06","key":"2342_CR36","doi-asserted-by":"publisher","first-page":"1137","DOI":"10.1109\/TPAMI.2016.2577031","volume":"39","author":"S Ren","year":"2017","unstructured":"Ren S, He K, Girshick R, Sun J (2017) Faster r-cnn: towards real-time object detection with region proposal networks. IEEE Trans Pattern Anal Mach Intell 39(06):1137\u20131149","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"2342_CR37","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":"2342_CR38","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":"2342_CR39","doi-asserted-by":"crossref","unstructured":"Wang J, Gao Y, Li H (2023) An interactive attention mechanism fusion network for aspect-based multimodal sentiment analysis. In: 2023 International Conference on Machine Learning and Cybernetics (ICMLC), pp 268\u2013275. IEEE","DOI":"10.1109\/ICMLC58545.2023.10327929"}],"container-title":["International Journal of Machine Learning and Cybernetics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s13042-024-02342-w.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s13042-024-02342-w\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s13042-024-02342-w.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,2,4]],"date-time":"2025-02-04T10:42:22Z","timestamp":1738665742000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s13042-024-02342-w"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,8,30]]},"references-count":39,"journal-issue":{"issue":"2","published-print":{"date-parts":[[2025,2]]}},"alternative-id":["2342"],"URL":"https:\/\/doi.org\/10.1007\/s13042-024-02342-w","relation":{},"ISSN":["1868-8071","1868-808X"],"issn-type":[{"value":"1868-8071","type":"print"},{"value":"1868-808X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,8,30]]},"assertion":[{"value":"22 March 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"17 August 2024","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"30 August 2024","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 no competing interests.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}