{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,26]],"date-time":"2026-03-26T10:58:42Z","timestamp":1774522722921,"version":"3.50.1"},"reference-count":44,"publisher":"Springer Science and Business Media LLC","issue":"3","license":[{"start":{"date-parts":[[2026,2,16]],"date-time":"2026-02-16T00:00:00Z","timestamp":1771200000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2026,2,16]],"date-time":"2026-02-16T00:00:00Z","timestamp":1771200000000},"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":["Int. J. Mach. Learn. &amp; Cyber."],"published-print":{"date-parts":[[2026,3]]},"DOI":"10.1007\/s13042-026-02991-z","type":"journal-article","created":{"date-parts":[[2026,2,16]],"date-time":"2026-02-16T10:15:09Z","timestamp":1771236909000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Multi-level perception cross-modal fusion framework for multimodal sentiment analysis"],"prefix":"10.1007","volume":"17","author":[{"given":"Lei","family":"Yu","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"AnZhan","family":"Liu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2026,2,16]]},"reference":[{"key":"2991_CR1","doi-asserted-by":"crossref","unstructured":"Al-Saqqa S, Abdel-Nabi H, Awajan A (2018) A survey of textual emotion detection. In: 2018 8th international conference on computer science and information technology (CSIT), pp. 136\u2013142 . IEEE","DOI":"10.1109\/CSIT.2018.8486405"},{"issue":"7","key":"2991_CR2","first-page":"1165","volume":"15","author":"L Jiming","year":"2021","unstructured":"Jiming L, Peixiang Z, Ying L, Weidong Z, Jie F (2021) Summary of multi-modal sentiment analysis technology. J Front Comput Sci Technol 15(7):1165","journal-title":"J Front Comput Sci Technol"},{"key":"2991_CR3","doi-asserted-by":"publisher","first-page":"102563","DOI":"10.1016\/j.displa.2023.102563","volume":"80","author":"S Lai","year":"2023","unstructured":"Lai S, Hu X, Xu H, Ren Z, Liu Z (2023) Multimodal sentiment analysis: a survey. Displays 80:102563","journal-title":"Displays"},{"key":"2991_CR4","doi-asserted-by":"crossref","unstructured":"Hazarika D, Zimmermann R, Poria S (2020) Misa: Modality-invariant and-specific representations for multimodal sentiment analysis. In: Proceedings of the 28th ACM international conference on multimedia, pp. 1122\u20131131","DOI":"10.1145\/3394171.3413678"},{"key":"2991_CR5","doi-asserted-by":"crossref","unstructured":"Yang D, Huang S, Kuang H, Du Y, Zhang L (2022) Disentangled representation learning for multimodal emotion recognition. In: Proceedings of the 30th ACM international conference on multimedia, pp. 1642\u20131651","DOI":"10.1145\/3503161.3547754"},{"key":"2991_CR6","doi-asserted-by":"crossref","unstructured":"Yu W, Xu H, Yuan Z, Wu J (2021) Learning modality-specific representations with self-supervised multi-task learning for multimodal sentiment analysis. In: Proceedings of the AAAI conference on artificial intelligence, vol.35, pp. 10790\u201310797","DOI":"10.1609\/aaai.v35i12.17289"},{"key":"2991_CR7","doi-asserted-by":"crossref","unstructured":"Han W, Chen H, Poria S (2021) Improving multimodal fusion with hierarchical mutual information maximization for multimodal sentiment analysis. In: Proceedings of the 2021 conference on empirical methods in natural language processing, pp. 9180\u20139192","DOI":"10.18653\/v1\/2021.emnlp-main.723"},{"key":"2991_CR8","doi-asserted-by":"crossref","unstructured":"Guo J, Tang J, Dai W, Ding Y, Kong W (2022) Dynamically adjust word representations using unaligned multimodal information. In: Proceedings of the 30th ACM international conference on multimedia, pp. 3394\u20133402","DOI":"10.1145\/3503161.3548137"},{"key":"2991_CR9","doi-asserted-by":"crossref","unstructured":"Zadeh A, Chen M, Poria S, Cambria E, Morency LP (2017) Tensor fusion network for multimodal sentiment analysis. In: Proceedings of the 2017 conference on empirical methods in natural language processing, pp. 1103\u20131114","DOI":"10.18653\/v1\/D17-1115"},{"key":"2991_CR10","doi-asserted-by":"crossref","unstructured":"Liu Z, Shen Y, Lakshminarasimhan VB, Liang PP, Zadeh AB, Morency LP (2018) Efficient low-rank multimodal fusion with modality-specific factors. In: Proceedings of the 56th annual meeting of the association for computational linguistics (Volume 1: Long Papers), pp. 2247\u20132256","DOI":"10.18653\/v1\/P18-1209"},{"key":"2991_CR11","doi-asserted-by":"crossref","unstructured":"Tsai YHH, Bai S, Liang PP, Kolter JZ, Morency, LP, Salakhutdinov R (2019) Multimodal transformer for unaligned multimodal language sequences. In: Proceedings of the Conference. Association for Computational Linguistics. Meeting, vol.2019, p. 6558 . NIH Public Access","DOI":"10.18653\/v1\/P19-1656"},{"key":"2991_CR12","doi-asserted-by":"crossref","unstructured":"Huang J, Tao J, Liu B, Lian Z, Niu M (2020) Multimodal transformer fusion for continuous emotion recognition. In: ICASSP 2020-2020 IEEE international conference on acoustics, speech and signal processing (ICASSP), pp. 3507\u20133511. IEEE","DOI":"10.1109\/ICASSP40776.2020.9053762"},{"key":"2991_CR13","doi-asserted-by":"crossref","unstructured":"Zhang H, Wang Y, Yin G, Liu K, Liu Y, Yu T (2023) Learning language-guided adaptive hyper-modality representation for multimodal sentiment analysis. In: Proceedings of the 2023 conference on empirical methods in natural language processing, pp. 756\u2013767","DOI":"10.18653\/v1\/2023.emnlp-main.49"},{"key":"2991_CR14","doi-asserted-by":"crossref","unstructured":"Rahman W, Hasan MK, Lee S, Zadeh A, Mao C, Morency LP, Hoque E (2020) Integrating multimodal information in large pretrained transformers. In: Proceedings of the Conference. Association for computational linguistics. Meeting, vol.2020, p. 2359. NIH Public Access","DOI":"10.18653\/v1\/2020.acl-main.214"},{"issue":"1","key":"2991_CR15","doi-asserted-by":"publisher","first-page":"309","DOI":"10.1109\/TAFFC.2023.3274829","volume":"15","author":"L Sun","year":"2023","unstructured":"Sun L, Lian Z, Liu B, Tao J (2023) Efficient multimodal transformer with dual-level feature restoration for robust multimodal sentiment analysis. IEEE Trans Affect Comput 15(1):309\u2013325","journal-title":"IEEE Trans Affect Comput"},{"key":"2991_CR16","doi-asserted-by":"crossref","unstructured":"Shi T, Huang SL (2023) Multiemo: An attention-based correlation-aware multimodal fusion framework for emotion recognition in conversations. In: Proceedings of the 61st annual meeting of the association for computational linguistics (Volume 1: Long Papers), pp. 14752\u201314766","DOI":"10.18653\/v1\/2023.acl-long.824"},{"key":"2991_CR17","unstructured":"Luo H, Ji L, Huang Y, Wang B, Ji S, Li T (2021) Scalevlad: Improving multimodal sentiment analysis via multi-scale fusion of locally descriptors. arXiv preprint arXiv:2112.01368"},{"key":"2991_CR18","doi-asserted-by":"crossref","unstructured":"Liang T, Lin G, Feng L, Zhang Y, Lv F (2021) Attention is not enough: Mitigating the distribution discrepancy in asynchronous multimodal sequence fusion. In: Proceedings of the IEEE\/CVF international conference on computer vision, pp. 8148\u20138156","DOI":"10.1109\/ICCV48922.2021.00804"},{"key":"2991_CR19","unstructured":"Vaswani A, Shazeer N, Parmar N, Uszkoreit J, Jones L, Gomez AN, Kaiser \u0141, Polosukhin I (2017) Attention is all you need. advances in neural information processing systems. Adv Neural Inform Process Syst 30"},{"key":"2991_CR20","doi-asserted-by":"crossref","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: human language technologies, Volume 1 (long and Short Papers), pp. 4171\u20134186","DOI":"10.18653\/v1\/N19-1423"},{"key":"2991_CR21","doi-asserted-by":"crossref","unstructured":"Carion N, Massa F, Synnaeve G, Usunier N, Kirillov A, Zagoruyko S (2020) End-to-end object detection with transformers. In: European conference on computer vision, pp. 213\u2013229 . Springer","DOI":"10.1007\/978-3-030-58452-8_13"},{"key":"2991_CR22","doi-asserted-by":"crossref","unstructured":"Chen W, Xing X, Xu X, Pang J, Du L (2022) Speechformer: A hierarchical efficient framework incorporating the characteristics of speech. In: Proc. Interspeech 2022, pp. 346\u2013350","DOI":"10.21437\/Interspeech.2022-74"},{"key":"2991_CR23","doi-asserted-by":"publisher","first-page":"109368","DOI":"10.1016\/j.patcog.2023.109368","volume":"138","author":"Y Liu","year":"2023","unstructured":"Liu Y, Wang W, Feng C, Zhang H, Chen Z, Zhan Y (2023) Expression snippet transformer for robust video-based facial expression recognition. Pattern Recogn 138:109368","journal-title":"Pattern Recogn"},{"key":"2991_CR24","unstructured":"Kenton JDMWC, Toutanova LK (2019) Bert: Pre-training of deep bidirectional transformers for language understanding. In: Proceedings of NAACL-HLT, pp. 4171\u20134186"},{"key":"2991_CR25","unstructured":"Yang Z, Dai Z, Yang Y, Carbonell J, Salakhutdinov RR, Le QV (2019) Xlnet: Generalized autoregressive pretraining for language understanding. Adv Neural Inform Process Syst 32"},{"key":"2991_CR26","unstructured":"Liu Y(2019) Roberta: A robustly optimized bert pretraining approach. arXiv preprint arXiv:1907.11692364"},{"key":"2991_CR27","unstructured":"Zadeh A, Zellers R, Pincus E, Morency LP (2016) Mosi: multimodal corpus of sentiment intensity and subjectivity analysis in online opinion videos. arXiv preprint arXiv:1606.06259"},{"key":"2991_CR28","doi-asserted-by":"crossref","unstructured":"Zadeh AB, Liang PP, Poria S, Cambria E, Morency LP (2018) Multimodal language analysis in the wild: Cmu-mosei dataset and interpretable dynamic fusion graph. In: Proceedings of the 56th annual meeting of the association for computational linguistics (Volume 1: Long Papers), pp. 2236\u20132246","DOI":"10.18653\/v1\/P18-1208"},{"key":"2991_CR29","doi-asserted-by":"crossref","unstructured":"Yu W, Xu H, Meng F, Zhu Y, Ma Y, Wu J, Zou J, Yang K (2020) Ch-sims: a chinese multimodal sentiment analysis dataset with fine-grained annotation of modality, 3718\u20133727","DOI":"10.18653\/v1\/2020.acl-main.343"},{"issue":"7","key":"2991_CR30","doi-asserted-by":"publisher","first-page":"9814","DOI":"10.1109\/TII.2024.3388670","volume":"20","author":"R Hu","year":"2024","unstructured":"Hu R, Yi J, Chen A, Chen L (2024) Multichannel cross-modal fusion network for multimodal sentiment analysis considering language information enhancement. IEEE Trans Industr Inf 20(7):9814\u20139824","journal-title":"IEEE Trans Industr Inf"},{"key":"2991_CR31","unstructured":"Zhang M, Lucas J, Ba J, Hinton GE (2019) Lookahead optimizer: k steps forward, 1 step back. Adv Neural Inform Process Syst 32"},{"key":"2991_CR32","doi-asserted-by":"crossref","unstructured":"Baltrusaitis T, Zadeh A, Lim YC, Morency LP (2018) Openface 2.0: Facial behavior analysis toolkit. In: 2018 13th IEEE international conference on automatic face & gesture recognition (FG 2018), pp. 59\u201366","DOI":"10.1109\/FG.2018.00019"},{"key":"2991_CR33","doi-asserted-by":"crossref","unstructured":"McFee Raffel Liang PW.\u00a0Ellis, McVicar, Battenberg Nieto (2015) librosa: Audio and Music Signal Analysis in Python. In: Huff, Bergstra (eds.) Proceedings of the 14th python in science conference, pp. 18\u201324","DOI":"10.25080\/Majora-7b98e3ed-003"},{"key":"2991_CR34","doi-asserted-by":"crossref","unstructured":"Sun H, Wang H, Liu J, Chen YW, Lin L (2022) Cubemlp: An mlp-based model for multimodal sentiment analysis and depression estimation. In: Proceedings of the 30th ACM international conference on multimedia, pp. 3722\u20133729","DOI":"10.1145\/3503161.3548025"},{"key":"2991_CR35","doi-asserted-by":"publisher","first-page":"111346","DOI":"10.1016\/j.knosys.2023.111346","volume":"285","author":"J Huang","year":"2024","unstructured":"Huang J, Zhou J, Tang Z, Lin J, Chen CY-C (2024) Tmbl: Transformer-based multimodal binding learning model for multimodal sentiment analysis. Knowl-Based Syst 285:111346","journal-title":"Knowl-Based Syst"},{"key":"2991_CR36","doi-asserted-by":"crossref","unstructured":"Han W, Chen H, Gelbukh A, Zadeh A, Morency Lp, Poria S (2021) Bi-bimodal modality fusion for correlation-controlled multimodal sentiment analysis. In: Proceedings of the 2021 international conference on multimodal interaction, pp. 6\u201315","DOI":"10.1145\/3462244.3479919"},{"key":"2991_CR37","doi-asserted-by":"publisher","first-page":"109259","DOI":"10.1016\/j.patcog.2022.109259","volume":"136","author":"D Wang","year":"2023","unstructured":"Wang D, Guo X, Tian Y, Liu J, He L, Luo X (2023) Tetfn: a text enhanced transformer fusion network for multimodal sentiment analysis. Pattern Recogn 136:109259","journal-title":"Pattern Recogn"},{"key":"2991_CR38","doi-asserted-by":"crossref","unstructured":"Zhu C, Chen M, Li H, Zhang S, Liang H, Sun C, Liu Y, Chen J (2024) Multi-level multi-task representation learning with adaptive fusion for multimodal sentiment analysis. Neural Comput Appl, 1\u201318","DOI":"10.1007\/s00521-024-10678-1"},{"key":"2991_CR39","doi-asserted-by":"publisher","first-page":"102787","DOI":"10.1016\/j.inffus.2024.102787","volume":"116","author":"L Zhu","year":"2025","unstructured":"Zhu L, Zhao H, Zhu Z, Zhang C, Kong X (2025) Multimodal sentiment analysis with unimodal label generation and modality decomposition. Inform Fusion 116:102787","journal-title":"Inform Fusion"},{"key":"2991_CR40","doi-asserted-by":"crossref","unstructured":"Feng X, Lin Y, He L, Li Y, Chang L, Zhou Y (2024) Knowledge-guided dynamic modality attention fusion framework for multimodal sentiment analysis. In: Findings of the association for computational linguistics: EMNLP 2024, pp. 14755\u201314766","DOI":"10.18653\/v1\/2024.findings-emnlp.865"},{"key":"2991_CR41","doi-asserted-by":"crossref","unstructured":"Zhao X, Chen Y, Li W, Gao L, Tang B (2022) Mag+: an extended multimodal adaptation gate for multimodal sentiment analysis. In: ICASSP 2022-2022 IEEE international conference on acoustics, speech and signal processing (ICASSP), pp. 4753\u20134757 . IEEE","DOI":"10.1109\/ICASSP43922.2022.9746536"},{"key":"2991_CR42","doi-asserted-by":"publisher","first-page":"101958","DOI":"10.1016\/j.inffus.2023.101958","volume":"100","author":"C Zhu","year":"2023","unstructured":"Zhu C, Chen M, Zhang S, Sun C, Liang H, Liu Y, Chen J (2023) Skeafn: sentiment knowledge enhanced attention fusion network for multimodal sentiment analysis. Information Fusion 100:101958","journal-title":"Information Fusion"},{"key":"2991_CR43","doi-asserted-by":"publisher","first-page":"101891","DOI":"10.1016\/j.inffus.2023.101891","volume":"99","author":"Z Li","year":"2023","unstructured":"Li Z, Guo Q, Pan Y, Ding W, Yu J, Zhang Y, Liu W, Chen H, Wang H, Xie Y (2023) Multi-level correlation mining framework with self-supervised label generation for multimodal sentiment analysis. Information Fusion 99:101891","journal-title":"Information Fusion"},{"key":"2991_CR44","doi-asserted-by":"publisher","first-page":"107676","DOI":"10.1016\/j.knosys.2021.107676","volume":"235","author":"T Wu","year":"2022","unstructured":"Wu T, Peng J, Zhang W, Zhang H, Tan S, Yi F, Ma C, Huang Y (2022) Video sentiment analysis with bimodal information-augmented multi-head attention. Knowl-Based Syst 235:107676","journal-title":"Knowl-Based Syst"}],"container-title":["International Journal of Machine Learning and Cybernetics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s13042-026-02991-z.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s13042-026-02991-z","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s13042-026-02991-z.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,3,26]],"date-time":"2026-03-26T10:02:56Z","timestamp":1774519376000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s13042-026-02991-z"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,2,16]]},"references-count":44,"journal-issue":{"issue":"3","published-print":{"date-parts":[[2026,3]]}},"alternative-id":["2991"],"URL":"https:\/\/doi.org\/10.1007\/s13042-026-02991-z","relation":{},"ISSN":["1868-8071","1868-808X"],"issn-type":[{"value":"1868-8071","type":"print"},{"value":"1868-808X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,2,16]]},"assertion":[{"value":"1 March 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"1 January 2026","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"16 February 2026","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 Conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}],"article-number":"91"}}