{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,20]],"date-time":"2026-07-20T16:07:22Z","timestamp":1784563642713,"version":"3.55.0"},"reference-count":64,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2026,10,1]],"date-time":"2026-10-01T00:00:00Z","timestamp":1790812800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2026,10,1]],"date-time":"2026-10-01T00:00:00Z","timestamp":1790812800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2026,10,1]],"date-time":"2026-10-01T00:00:00Z","timestamp":1790812800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-017"},{"start":{"date-parts":[[2026,10,1]],"date-time":"2026-10-01T00:00:00Z","timestamp":1790812800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"},{"start":{"date-parts":[[2026,10,1]],"date-time":"2026-10-01T00:00:00Z","timestamp":1790812800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-012"},{"start":{"date-parts":[[2026,10,1]],"date-time":"2026-10-01T00:00:00Z","timestamp":1790812800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2026,10,1]],"date-time":"2026-10-01T00:00:00Z","timestamp":1790812800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-004"}],"funder":[{"DOI":"10.13039\/501100012166","name":"National Key Research and Development Program of China","doi-asserted-by":"publisher","award":["2026YFE0201300"],"award-info":[{"award-number":["2026YFE0201300"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62576056"],"award-info":[{"award-number":["62576056"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62221005"],"award-info":[{"award-number":["62221005"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62276038"],"award-info":[{"award-number":["62276038"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Engineering Applications of Artificial Intelligence"],"published-print":{"date-parts":[[2026,10]]},"DOI":"10.1016\/j.engappai.2026.115552","type":"journal-article","created":{"date-parts":[[2026,7,4]],"date-time":"2026-07-04T12:00:56Z","timestamp":1783166456000},"page":"115552","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":0,"special_numbering":"P4","title":["Hierarchical joint fuzzy network for multimodal emotion recognition in conversations"],"prefix":"10.1016","volume":"181","author":[{"given":"Hang","family":"Zhong","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6154-4656","authenticated-orcid":false,"given":"Qinghua","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Fan","family":"Zhao","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ruili","family":"Guo","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","reference":[{"key":"10.1016\/j.engappai.2026.115552_b1","series-title":"Proceedings of the First Workshop on Performance and Interpretability Evaluations of Multimodal, Multipurpose, Massive-Scale Models","first-page":"44","article-title":"Shapes of emotions: Multimodal emotion recognition in conversations via emotion shifts","author":"Bansal","year":"2022"},{"key":"10.1016\/j.engappai.2026.115552_b2","first-page":"281","article-title":"Random search for hyper-parameter optimization","volume":"13","author":"Bergstra","year":"2012","journal-title":"J. Mach. Learn. Res."},{"key":"10.1016\/j.engappai.2026.115552_b3","doi-asserted-by":"crossref","first-page":"335","DOI":"10.1007\/s10579-008-9076-6","article-title":"IEMOCAP: Interactive emotional dyadic motion capture database","volume":"42","author":"Busso","year":"2008","journal-title":"Lang. Resour. Eval."},{"key":"10.1016\/j.engappai.2026.115552_b4","unstructured":"Cerisara, C., Jafaritazehjani, S., Oluokun, A., Le, H.T., 2018. Multi-task dialog act and sentiment recognition on mastodon. In: Proceedings of the 27th International Conference on Computational Linguistics. pp. 745\u2013754."},{"key":"10.1016\/j.engappai.2026.115552_b5","doi-asserted-by":"crossref","first-page":"264","DOI":"10.1016\/j.patrec.2019.04.024","article-title":"Fuzzy commonsense reasoning for multimodal sentiment analysis","volume":"125","author":"Chaturvedi","year":"2019","journal-title":"Pattern Recognit. Lett."},{"key":"10.1016\/j.engappai.2026.115552_b6","series-title":"Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing","first-page":"1724","article-title":"Learning phrase representations using RNN encoder-decoder for statistical machine translation","author":"Cho","year":"2014"},{"key":"10.1016\/j.engappai.2026.115552_b7","series-title":"Second Grand-Challenge and Workshop on Multimodal Language (Challenge-HML)","first-page":"1","article-title":"A transformer-based joint-encoding for emotion recognition and sentiment analysis","author":"Delbrouck","year":"2020"},{"key":"10.1016\/j.engappai.2026.115552_b8","series-title":"In Proceedings of the 3rd International Conference on Learning Representations","first-page":"7","article-title":"Adam: A method for stochastic optimization","author":"Diederik","year":"2015"},{"key":"10.1016\/j.engappai.2026.115552_b9","series-title":"HCAM\u2013hierarchical cross attention model for multi-modal emotion recognition","author":"Dutta","year":"2023"},{"key":"10.1016\/j.engappai.2026.115552_b10","series-title":"Proceedings of the 18th ACM International Conference on Multimedia","first-page":"1459","article-title":"Opensmile: the munich versatile and fast open-source audio feature extractor","author":"Eyben","year":"2010"},{"key":"10.1016\/j.engappai.2026.115552_b11","series-title":"Cross-modal context fusion and adaptive graph convolutional network for multimodal conversational emotion recognition","author":"Feng","year":"2025"},{"key":"10.1016\/j.engappai.2026.115552_b12","series-title":"CKERC: Joint large language models with commonsense knowledge for emotion recognition in conversation","author":"Fu","year":"2024"},{"key":"10.1016\/j.engappai.2026.115552_b13","doi-asserted-by":"crossref","unstructured":"Ghosal, D., Akhtar, M.S., Chauhan, D., Poria, S., Ekbal, A., Bhattacharyya, P., 2018. Contextual inter-modal attention for multi-modal sentiment analysis. In: Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing. pp. 3454\u20133466.","DOI":"10.18653\/v1\/D18-1382"},{"key":"10.1016\/j.engappai.2026.115552_b14","series-title":"Findings of the Association for Computational Linguistics: EMNLP 2020","first-page":"2470","article-title":"COSMIC: COmmonsense knowledge for emotion identification in conversations","author":"Ghosal","year":"2020"},{"key":"10.1016\/j.engappai.2026.115552_b15","series-title":"Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing","first-page":"154","article-title":"DialogueGCN: A graph convolutional neural network for emotion recognition in conversation","author":"Ghosal","year":"2019"},{"key":"10.1016\/j.engappai.2026.115552_b16","series-title":"Supervised Sequence Labelling with Recurrent Neural Networks","first-page":"37","article-title":"Long short-term memory","author":"Graves","year":"2012"},{"key":"10.1016\/j.engappai.2026.115552_b17","doi-asserted-by":"crossref","unstructured":"Hu, D., Bao, Y., Wei, L., Zhou, W., Hu, S., 2023. Supervised adversarial contrastive learning for emotion recognition in conversations. In: Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics. pp. 10835\u201310852.","DOI":"10.18653\/v1\/2023.acl-long.606"},{"key":"10.1016\/j.engappai.2026.115552_b18","series-title":"ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing","first-page":"7037","article-title":"MM-DFN: multimodal dynamic fusion network for emotion recognition in conversations","author":"Hu","year":"2022"},{"key":"10.1016\/j.engappai.2026.115552_b19","doi-asserted-by":"crossref","unstructured":"Hu, G., Lin, T.-E., Zhao, Y., Lu, G., Wu, Y., Li, Y., 2022. UniMSE: Towards unified multimodal sentiment analysis and emotion recognition. In: Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing. pp. 7837\u20137851.","DOI":"10.18653\/v1\/2022.emnlp-main.534"},{"key":"10.1016\/j.engappai.2026.115552_b20","series-title":"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing","first-page":"5666","article-title":"MMGCN: Multimodal fusion via deep graph convolution network for emotion recognition in conversation","author":"Hu","year":"2021"},{"key":"10.1016\/j.engappai.2026.115552_b21","series-title":"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing","first-page":"7042","article-title":"DialogueCRN: Contextual reasoning networks for emotion recognition in conversations","author":"Hu","year":"2021"},{"key":"10.1016\/j.engappai.2026.115552_b22","doi-asserted-by":"crossref","unstructured":"Huang, G., Liu, Z., Van Der Maaten, L., Weinberger, K.Q., 2017. Densely connected convolutional networks. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition. pp. 4700\u20134708.","DOI":"10.1109\/CVPR.2017.243"},{"issue":"5","key":"10.1016\/j.engappai.2026.115552_b23","doi-asserted-by":"crossref","first-page":"601","DOI":"10.1109\/3477.790443","article-title":"Performance evaluation of fuzzy classifier systems for multidimensional pattern classification problems","volume":"29","author":"Ishibuchi","year":"1999","journal-title":"IEEE Trans. Syst. Man Cybern. B"},{"issue":"10","key":"10.1016\/j.engappai.2026.115552_b24","doi-asserted-by":"crossref","first-page":"1482","DOI":"10.1109\/TAC.1997.633847","article-title":"Neuro-fuzzy and soft computing-a computational approach to learning and machine intelligence [book review]","volume":"42","author":"Jang","year":"1997","journal-title":"IEEE Trans. Autom. Control"},{"issue":"3","key":"10.1016\/j.engappai.2026.115552_b25","doi-asserted-by":"crossref","first-page":"1082","DOI":"10.1007\/s12559-023-10119-6","article-title":"CSAT-FTCN: a fuzzy-oriented model with contextual self-attention network for multimodal emotion recognition","volume":"15","author":"Jiang","year":"2023","journal-title":"Cogn. Comput."},{"key":"10.1016\/j.engappai.2026.115552_b26","doi-asserted-by":"crossref","unstructured":"Jiao, W., Yang, H., King, I., Lyu, M.R., 2019. HiGRU: Hierarchical gated recurrent units for utterance-level emotion recognition. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies. pp. 397\u2013406.","DOI":"10.18653\/v1\/N19-1037"},{"key":"10.1016\/j.engappai.2026.115552_b27","first-page":"471","article-title":"Serial order: A parallel distributed processing approach","volume":"vol. 121","author":"Jordan","year":"1997"},{"issue":"03n04","key":"10.1016\/j.engappai.2026.115552_b28","doi-asserted-by":"crossref","DOI":"10.1142\/S0218001425320015","article-title":"Deep learning-based texture feature extraction technique for face annotation","volume":"39","author":"Kasthuri","year":"2025","journal-title":"Int. J. Pattern Recognit. Artif. Intell."},{"issue":"2","key":"10.1016\/j.engappai.2026.115552_b29","doi-asserted-by":"crossref","first-page":"196","DOI":"10.1109\/TAFFC.2017.2702653","article-title":"ISLA: Temporal segmentation and labeling for audio-visual emotion recognition","volume":"10","author":"Kim","year":"2017","journal-title":"IEEE Trans. Affect. Comput."},{"key":"10.1016\/j.engappai.2026.115552_b30","series-title":"Emoberta: Speaker-aware emotion recognition in conversation with roberta","author":"Kim","year":"2021"},{"key":"10.1016\/j.engappai.2026.115552_b31","series-title":"2015 Eighth International Conference on Contemporary Computing","first-page":"285","article-title":"Emotion analysis of Twitter using opinion mining","author":"Kumar","year":"2015"},{"key":"10.1016\/j.engappai.2026.115552_b32","doi-asserted-by":"crossref","first-page":"1031","DOI":"10.3233\/IDA-205183","article-title":"Multimodal emotion recognition with hierarchical memory networks","volume":"25","author":"Lai","year":"2021","journal-title":"Intell. Data Anal."},{"key":"10.1016\/j.engappai.2026.115552_b33","doi-asserted-by":"crossref","unstructured":"Lee, J., Lee, W., 2022. CoMPM: Context modeling with speaker\u2019s pre-trained memory tracking for emotion recognition in conversation. In: Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies. pp. 5669\u20135679.","DOI":"10.18653\/v1\/2022.naacl-main.416"},{"key":"10.1016\/j.engappai.2026.115552_b34","doi-asserted-by":"crossref","unstructured":"Li, J., Ji, D., Li, F., Zhang, M., Liu, Y., 2020. HiTrans: A transformer-based context-and speaker-sensitive model for emotion detection in conversations. In: Proceedings of the 28th International Conference on Computational Linguistics. pp. 4190\u20134200.","DOI":"10.18653\/v1\/2020.coling-main.370"},{"key":"10.1016\/j.engappai.2026.115552_b35","doi-asserted-by":"crossref","first-page":"77","DOI":"10.1109\/TMM.2023.3260635","article-title":"GraphCFC: A directed graph based cross-modal feature complementation approach for multimodal conversational emotion recognition","volume":"26","author":"Li","year":"2023","journal-title":"IEEE Trans. Multimed."},{"key":"10.1016\/j.engappai.2026.115552_b36","series-title":"Long-short distance graph neural networks and improved curriculum learning for emotion recognition in conversation","author":"Li","year":"2025"},{"key":"10.1016\/j.engappai.2026.115552_b37","series-title":"Roberta: A robustly optimized bert pretraining approach","author":"Liu","year":"2019"},{"key":"10.1016\/j.engappai.2026.115552_b38","doi-asserted-by":"crossref","unstructured":"Lopez, E., Uncini, A., Comminiello, D., 2024. Hierarchical Hypercomplex Network for Multimodal Emotion Recognition. In: 2024 IEEE 34th International Workshop on Machine Learning for Signal Processing. MLSP, pp. 1\u20136.","DOI":"10.1109\/MLSP58920.2024.10734815"},{"key":"10.1016\/j.engappai.2026.115552_b39","first-page":"6818","article-title":"DialogueRNN: An attentive rnn for emotion detection in conversations","volume":"vol. 33, no. 01","author":"Majumder","year":"2019"},{"key":"10.1016\/j.engappai.2026.115552_b40","series-title":"Findings of the Association for Computational Linguistics: EMNLP 2021","first-page":"2694","article-title":"DialogueTRM: Exploring the intra-and inter-modal emotional behaviors in the conversation","author":"Mao","year":"2021"},{"key":"10.1016\/j.engappai.2026.115552_b41","doi-asserted-by":"crossref","first-page":"4298","DOI":"10.1109\/TASLP.2024.3434495","article-title":"Masked graph learning with recurrent alignment for multimodal emotion recognition in conversation","volume":"32","author":"Meng","year":"2024","journal-title":"IEEE\/ACM Trans. Audio Speech Lang. Process."},{"key":"10.1016\/j.engappai.2026.115552_b42","doi-asserted-by":"crossref","first-page":"208","DOI":"10.1016\/j.neunet.2019.06.010","article-title":"A multimodal convolutional neuro-fuzzy network for emotion understanding of movie clips","volume":"118","author":"Nguyen","year":"2019","journal-title":"Neural Netw."},{"key":"10.1016\/j.engappai.2026.115552_b43","series-title":"Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics","first-page":"873","article-title":"Context-dependent sentiment analysis in user-generated videos","author":"Poria","year":"2017"},{"key":"10.1016\/j.engappai.2026.115552_b44","doi-asserted-by":"crossref","unstructured":"Poria, S., Hazarika, D., Majumder, N., Naik, G., Cambria, E., Mihalcea, R., 2019. MELD: A multimodal multi-party dataset for emotion recognition in conversations. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics. pp. 527\u2013536.","DOI":"10.18653\/v1\/P19-1050"},{"key":"10.1016\/j.engappai.2026.115552_b45","series-title":"Research & Innovation Forum 2019: Technology, Innovation, Education, and their Social Impact 1","first-page":"245","article-title":"Emotion recognition to improve e-healthcare systems in smart cities","author":"Pujol","year":"2019"},{"key":"10.1016\/j.engappai.2026.115552_b46","doi-asserted-by":"crossref","DOI":"10.1016\/j.neucom.2024.128646","article-title":"MFGCN: Multimodal fusion graph convolutional network for speech emotion recognition","volume":"611","author":"Qi","year":"2025","journal-title":"Neurocomputing"},{"key":"10.1016\/j.engappai.2026.115552_b47","first-page":"13492","article-title":"Bert-erc: Fine-tuning bert is enough for emotion recognition in conversation","volume":"vol. 37, no. 11","author":"Qin","year":"2023"},{"issue":"01","key":"10.1016\/j.engappai.2026.115552_b48","doi-asserted-by":"crossref","DOI":"10.1142\/S0219467824500591","article-title":"An efficient classification of multiclass brain tumor image using hybrid artificial intelligence with honey bee optimization and probabilistic U-RSNet","volume":"25","author":"Ramamoorthy","year":"2025","journal-title":"Int. J. Image Graph."},{"key":"10.1016\/j.engappai.2026.115552_b49","series-title":"Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics","first-page":"5370","article-title":"Towards empathetic open-domain conversation models: A new benchmark and dataset","author":"Rashkin","year":"2019"},{"issue":"1","key":"10.1016\/j.engappai.2026.115552_b50","doi-asserted-by":"crossref","first-page":"45","DOI":"10.1109\/MCI.2018.2881643","article-title":"Fuzzy clustering: A historical perspective","volume":"14","author":"Ruspini","year":"2019","journal-title":"IEEE Comput. Intell. Mag."},{"key":"10.1016\/j.engappai.2026.115552_b51","doi-asserted-by":"crossref","DOI":"10.1016\/j.knosys.2021.107018","article-title":"Fuzzy logic applied to opinion mining: a review","volume":"222","author":"Serrano-Guerrero","year":"2021","journal-title":"Knowl.-Based Syst."},{"key":"10.1016\/j.engappai.2026.115552_b52","first-page":"13789","article-title":"DialogXL: All-in-one xlnet for multi-party conversation emotion recognition","volume":"vol. 35, no. 15","author":"Shen","year":"2021"},{"key":"10.1016\/j.engappai.2026.115552_b53","series-title":"Multilogue-net: A context aware rnn for multi-modal emotion detection and sentiment analysis in conversation","author":"Shenoy","year":"2020"},{"key":"10.1016\/j.engappai.2026.115552_b54","series-title":"Efficient long-distance latent relation-aware graph neural network for multi-modal emotion recognition in conversations","author":"Shou","year":"2024"},{"key":"10.1016\/j.engappai.2026.115552_b55","first-page":"1","article-title":"EBPGA: Extractive text summarization using binary particle swarm optimization and masked genetic algorithm","author":"Siranjeevi","year":"2025","journal-title":"IEEE Trans. Comput. Soc. Syst."},{"key":"10.1016\/j.engappai.2026.115552_b56","doi-asserted-by":"crossref","DOI":"10.1016\/j.inffus.2024.102382","article-title":"HiCMAE: Hierarchical contrastive masked autoencoder for self-supervised audio-visual emotion recognition","volume":"108","author":"Sun","year":"2024","journal-title":"Inf. Fusion"},{"key":"10.1016\/j.engappai.2026.115552_b57","series-title":"2022 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference","first-page":"1288","article-title":"Fusion with hierarchical graphs for multimodal emotion recognition","author":"Tang","year":"2022"},{"key":"10.1016\/j.engappai.2026.115552_b58","series-title":"International Conference on Artificial Neural Networks","first-page":"277","article-title":"BiosERC: Integrating biography speakers supported by LLMs for erc tasks","author":"Xue","year":"2024"},{"key":"10.1016\/j.engappai.2026.115552_b59","doi-asserted-by":"crossref","unstructured":"Yun, T., Lim, H., Lee, J., Song, M., 2024. TelME: Teacher-leading Multimodal Fusion Network for Emotion Recognition in Conversation. In: Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies. pp. 82\u201395.","DOI":"10.18653\/v1\/2024.naacl-long.5"},{"issue":"3","key":"10.1016\/j.engappai.2026.115552_b60","doi-asserted-by":"crossref","first-page":"338","DOI":"10.1016\/S0019-9958(65)90241-X","article-title":"Fuzzy sets","volume":"8","author":"Zadeh","year":"1965","journal-title":"Inf. Control"},{"key":"10.1016\/j.engappai.2026.115552_b61","doi-asserted-by":"crossref","unstructured":"Zadeh, A.B., Liang, P.P., Poria, S., Cambria, E., Morency, L.-P., 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"},{"issue":"12","key":"10.1016\/j.engappai.2026.115552_b62","doi-asserted-by":"crossref","first-page":"3696","DOI":"10.1109\/TFUZZ.2021.3072492","article-title":"CFN: a complex-valued fuzzy network for sarcasm detection in conversations","volume":"29","author":"Zhang","year":"2021","journal-title":"IEEE Trans. Fuzzy Syst."},{"issue":"3","key":"10.1016\/j.engappai.2026.115552_b63","first-page":"1377","article-title":"Survey on emotional dialogue techniques","volume":"35","author":"Zhao","year":"2023","journal-title":"J. Softw."},{"key":"10.1016\/j.engappai.2026.115552_b64","unstructured":"Zhao, W., Zhao, Y., Qin, B., 2022. MuCDN: Mutual conversational detachment network for emotion recognition in multi-party conversations. In: Proceedings of the 29th International Conference on Computational Linguistics. pp. 7020\u20137030."}],"container-title":["Engineering Applications of Artificial Intelligence"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0952197626018361?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0952197626018361?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,7,20]],"date-time":"2026-07-20T15:41:21Z","timestamp":1784562081000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S0952197626018361"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,10]]},"references-count":64,"alternative-id":["S0952197626018361"],"URL":"https:\/\/doi.org\/10.1016\/j.engappai.2026.115552","relation":{},"ISSN":["0952-1976"],"issn-type":[{"value":"0952-1976","type":"print"}],"subject":[],"published":{"date-parts":[[2026,10]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"Hierarchical joint fuzzy network for multimodal emotion recognition in conversations","name":"articletitle","label":"Article Title"},{"value":"Engineering Applications of Artificial Intelligence","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.engappai.2026.115552","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2026 Elsevier Ltd. All rights are reserved, including those for text and data mining, AI training, and similar technologies.","name":"copyright","label":"Copyright"}],"article-number":"115552"}}