{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,26]],"date-time":"2025-03-26T20:33:54Z","timestamp":1743021234970,"version":"3.40.3"},"publisher-location":"Singapore","reference-count":31,"publisher":"Springer Nature Singapore","isbn-type":[{"type":"print","value":"9789819984282"},{"type":"electronic","value":"9789819984299"}],"license":[{"start":{"date-parts":[[2023,12,24]],"date-time":"2023-12-24T00:00:00Z","timestamp":1703376000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,12,24]],"date-time":"2023-12-24T00:00:00Z","timestamp":1703376000000},"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":[],"published-print":{"date-parts":[[2024]]},"DOI":"10.1007\/978-981-99-8429-9_30","type":"book-chapter","created":{"date-parts":[[2023,12,23]],"date-time":"2023-12-23T08:02:17Z","timestamp":1703318537000},"page":"370-382","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Co-attention Guided Local-Global Feature Fusion for\u00a0Aspect-Level Multimodal Sentiment Analysis"],"prefix":"10.1007","author":[{"given":"Guoyong","family":"Cai","sequence":"first","affiliation":[]},{"given":"Shunjie","family":"Wang","sequence":"additional","affiliation":[]},{"given":"Guangrui","family":"Lv","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2023,12,24]]},"reference":[{"key":"30_CR1","doi-asserted-by":"crossref","unstructured":"Truong, Q.T., Lauw, H.W.: VistaNet: visual aspect attention network for multimodal sentiment analysis. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 33, no. 01, pp. 305\u2013312 (2019)","DOI":"10.1609\/aaai.v33i01.3301305"},{"key":"30_CR2","doi-asserted-by":"crossref","unstructured":"Xu, N., Mao, W., Chen, G.: Multi-interactive memory network for aspect based multimodal sentiment analysis. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 33, no. 01, pp. 371\u2013378 (2019)","DOI":"10.1609\/aaai.v33i01.3301371"},{"key":"30_CR3","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.: Entity-sensitive attention and fusion network for entity-level multimodal sentiment classification. IEEE\/ACM Trans. Audio Speech Lang. Process. 28, 429\u2013439 (2019)","journal-title":"IEEE\/ACM Trans. Audio Speech Lang. Process."},{"key":"30_CR4","doi-asserted-by":"publisher","first-page":"157329","DOI":"10.1109\/ACCESS.2021.3126782","volume":"9","author":"D Gu","year":"2021","unstructured":"Gu, D., Wang, J., Cai, S.: Targeted aspect-based multimodal sentiment analysis: an attention capsule extraction and multi-head fusion network. IEEE Access 9, 157329\u2013157336 (2021)","journal-title":"IEEE Access"},{"key":"30_CR5","doi-asserted-by":"crossref","unstructured":"Xu, N., Mao, W., Chen, G.: A co-memory network for multimodal sentiment analysis. In: the 41st International ACM SIGIR Conference on Research & Development in Information Retrieval, pp. 929\u2013932 (2018)","DOI":"10.1145\/3209978.3210093"},{"key":"30_CR6","doi-asserted-by":"publisher","first-page":"172948","DOI":"10.1109\/ACCESS.2019.2955637","volume":"7","author":"S Nemati","year":"2019","unstructured":"Nemati, S., Rohani, R., Basiri, M.E.: A hybrid latent space data fusion method for multimodal emotion recognition. IEEE Access 7, 172948\u2013172964 (2019)","journal-title":"IEEE Access"},{"issue":"2","key":"30_CR7","doi-asserted-by":"publisher","first-page":"41","DOI":"10.3390\/a9020041","volume":"9","author":"Y Yu","year":"2016","unstructured":"Yu, Y., Lin, H., Meng, J.: Visual and textual sentiment analysis of a microblog using deep convolutional neural networks. Algorithms 9(2), 41 (2016)","journal-title":"Algorithms"},{"issue":"1","key":"30_CR8","doi-asserted-by":"publisher","DOI":"10.1016\/j.ipm.2019.102141","volume":"57","author":"A Kumar","year":"2020","unstructured":"Kumar, A., Srinivasan, K., Cheng, W.H.: Hybrid context enriched deep learning model for fine-grained sentiment analysis in textual and visual semiotic modality social data. Inf. Process. Manag. 57(1), 102141 (2020)","journal-title":"Inf. Process. Manag."},{"key":"30_CR9","doi-asserted-by":"crossref","unstructured":"Chen, F., Gao, Y., Cao, D.: Multimodal hypergraph learning for microblog sentiment prediction. In: 2015 IEEE International Conference on Multimedia and Expo (ICME), pp. 1\u20136 (2015)","DOI":"10.1109\/ICME.2015.7177477"},{"key":"30_CR10","doi-asserted-by":"publisher","first-page":"387","DOI":"10.1016\/j.asoc.2019.04.010","volume":"80","author":"J Xu","year":"2019","unstructured":"Xu, J., Huang, F., Zhang, X.: Sentiment analysis of social images via hierarchical deep fusion of content and links. Appl. Soft Comput. 80, 387\u2013399 (2019)","journal-title":"Appl. Soft Comput."},{"key":"30_CR11","doi-asserted-by":"crossref","unstructured":"Zhang, L., Wang, S., Liu, B.: Deep learning for sentiment analysis: a survey. Wiley Interdisc. Rev. Data Min. Knowl. Discov. 8(4) (2018)","DOI":"10.1002\/widm.1253"},{"key":"30_CR12","doi-asserted-by":"crossref","unstructured":"Dong, L., Wei, F., Tan, C.: Adaptive recursive neural network for target-dependent Twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49\u201354 (2014)","DOI":"10.3115\/v1\/P14-2009"},{"key":"30_CR13","unstructured":"Tang, D., Qin, B., Feng, X., Liu, T.: Effective LSTMs for target-dependent sentiment classification. In: Proceedings of the 26th International Conference on Computational Linguistics: Technical Papers, pp. 3298\u20133307 (2016)"},{"key":"30_CR14","doi-asserted-by":"crossref","unstructured":"Li, R., Chen, H., Feng, F., Ma, Z., Wang, X., Hovy, E.: 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, pp. 6319\u20136329 (2021)","DOI":"10.18653\/v1\/2021.acl-long.494"},{"key":"30_CR15","doi-asserted-by":"crossref","unstructured":"Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. In: Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing, pp. 214\u2013224 (2016)","DOI":"10.18653\/v1\/D16-1021"},{"key":"30_CR16","doi-asserted-by":"crossref","unstructured":"Chen, P., Sun, Z., Bing, L.: 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 (2017)","DOI":"10.18653\/v1\/D17-1047"},{"key":"30_CR17","doi-asserted-by":"crossref","unstructured":"Zhao, S., et al.: An end-to-end visual-audio attention network for emotion recognition in user-generated videos. In: Proceedings of the AAAI Conference on Artificial Intelligence, pp. 303\u2013311 (2020)","DOI":"10.1609\/aaai.v34i01.5364"},{"key":"30_CR18","doi-asserted-by":"crossref","unstructured":"Zadeh, A., Chen, M., Poria, S., Cambria, E., Morency, L.P.: Tensor fusion network for multimodal sentiment analysis. In: Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing, pp. 1103\u20131114 (2017)","DOI":"10.18653\/v1\/D17-1115"},{"key":"30_CR19","doi-asserted-by":"crossref","unstructured":"Yu, W., Xu, H., Yuan, Z., Wu, J.: Learning modality-specific representations with self-supervised multi-task learning for multimodal sentiment analysis. In: Proceedings of the AAAI Conference on Artificial Intelligence, pp. 10790\u201310797 (2021)","DOI":"10.1609\/aaai.v35i12.17289"},{"key":"30_CR20","doi-asserted-by":"crossref","unstructured":"Zhang, Z., Yang, J.: Temporal sentiment localization: listen and look in untrimmed videos. In: Proceedings of the 30th ACM International Conference on Multimedia, pp. 199\u2013208 (2022)","DOI":"10.1145\/3503161.3548007"},{"key":"30_CR21","doi-asserted-by":"crossref","unstructured":"Zhang, Z., Wang, L., Yang, J.: Weakly supervised video emotion detection and prediction via cross-modal temporal erasing network. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 18888\u201318897 (2023)","DOI":"10.1109\/CVPR52729.2023.01811"},{"key":"30_CR22","doi-asserted-by":"crossref","unstructured":"Yu, J., Jiang, J.: Adapting BERT for target-oriented multimodal sentiment classification. In: Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence, pp. 5408\u20135414 (2019)","DOI":"10.24963\/ijcai.2019\/751"},{"key":"30_CR23","doi-asserted-by":"crossref","unstructured":"Khan, Z., Fu, Y.: Exploiting BERT for multimodal target sentiment classification through input space translation. In: Proceedings of the 29th ACM International Conference on Multimedia, pp. 3034\u20133042 (2021)","DOI":"10.1145\/3474085.3475692"},{"key":"30_CR24","doi-asserted-by":"crossref","unstructured":"Yang, L., Na, J.-C., Yu, J.: Cross-modal multi task transformer for end-to-end multimodal aspect based sentiment analysis. Inf. Process. Manag. 59(5) (2022)","DOI":"10.1016\/j.ipm.2022.103038"},{"key":"30_CR25","doi-asserted-by":"crossref","unstructured":"Jia, L., Ma, T., Rong, H., Al-Nabhan, N.: Affective region recognition and fusion network for target-level multimodal sentiment classification. IEEE Trans. Emerg. Top. Comput. 0(1) (2023)","DOI":"10.1109\/TETC.2022.3231746"},{"key":"30_CR26","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770\u2013778 (2016)","DOI":"10.1109\/CVPR.2016.90"},{"key":"30_CR27","doi-asserted-by":"crossref","unstructured":"Chen, C., Han, D., Chang, C.C.: CAAN: context-aware attention network for visual question answering. Pattern Recogn. 132 (2022)","DOI":"10.1016\/j.patcog.2022.108980"},{"key":"30_CR28","doi-asserted-by":"crossref","unstructured":"Liu, Y., Liu, H., Wang, H., Meng, F., Liu, M.: BCAN: bidirectional correct attention network for cross-modal retrieval. IEEE Trans. Neural Netw. Learn. Syst. (2023)","DOI":"10.1109\/TNNLS.2023.3276796"},{"key":"30_CR29","unstructured":"Kingma, D.P., Ba, J.: Adam: a method for stochastic optimization. arXiv preprint arXiv:1412.6980 (2014)"},{"key":"30_CR30","doi-asserted-by":"crossref","unstructured":"Fan, F., Feng, Y., Zhao, D.: 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 (2018)","DOI":"10.18653\/v1\/D18-1380"},{"key":"30_CR31","doi-asserted-by":"crossref","unstructured":"Hazarika, D., Poria, S., Zadeh, A., Cambria, E., Morency, L.P., Zimmermann, R.: Conversational memory network for emotion recognition in dyadic dialogue videos. In: Proceedings of the Conference. Association for Computational Linguistics. North American Chapter. Meeting, p. 2122 (2018)","DOI":"10.18653\/v1\/N18-1193"}],"container-title":["Lecture Notes in Computer Science","Pattern Recognition and Computer Vision"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-981-99-8429-9_30","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,11,6]],"date-time":"2024-11-06T19:34:50Z","timestamp":1730921690000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-981-99-8429-9_30"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,12,24]]},"ISBN":["9789819984282","9789819984299"],"references-count":31,"URL":"https:\/\/doi.org\/10.1007\/978-981-99-8429-9_30","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2023,12,24]]},"assertion":[{"value":"24 December 2023","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"PRCV","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Chinese Conference on Pattern Recognition and Computer Vision (PRCV)","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Xiamen","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"China","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2023","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"13 October 2023","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"15 October 2023","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"6","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"ccprcv2023","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/prcv2023.xmu.edu.cn\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Double-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Microsoft CMT","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"1420","order":3,"name":"number_of_submissions_sent_for_review","label":"Number of Submissions Sent for Review","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"532","order":4,"name":"number_of_full_papers_accepted","label":"Number of Full Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"0","order":5,"name":"number_of_short_papers_accepted","label":"Number of Short Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"37% - The value is computed by the equation \"Number of Full Papers Accepted \/ Number of Submissions Sent for Review * 100\" and then rounded to a whole number.","order":6,"name":"acceptance_rate_of_full_papers","label":"Acceptance Rate of Full Papers","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"3,78","order":7,"name":"average_number_of_reviews_per_paper","label":"Average Number of Reviews per Paper","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"3,69","order":8,"name":"average_number_of_papers_per_reviewer","label":"Average Number of Papers per Reviewer","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"No","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}