{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,3]],"date-time":"2026-03-03T16:32:18Z","timestamp":1772555538960,"version":"3.50.1"},"publisher-location":"New York, NY, USA","reference-count":17,"publisher":"ACM","license":[{"start":{"date-parts":[[2022,9,23]],"date-time":"2022-09-23T00:00:00Z","timestamp":1663891200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2022,9,23]]},"DOI":"10.1145\/3573942.3573971","type":"proceedings-article","created":{"date-parts":[[2023,5,16]],"date-time":"2023-05-16T23:45:42Z","timestamp":1684280742000},"page":"187-192","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":2,"title":["BERT-based Multimodal Aspect-Level Sentiment Analysis for Social Media"],"prefix":"10.1145","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-4425-7927","authenticated-orcid":false,"given":"Zhe","family":"Wang","sequence":"first","affiliation":[{"name":"Center for Image and Information Processing, Xi'an University of Posts and Telecommunications, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9037-7818","authenticated-orcid":false,"given":"Ying","family":"Liu","sequence":"additional","affiliation":[{"name":"Center for Image and Information Processing, Xi'an University of Posts and Telecommunications, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9073-6534","authenticated-orcid":false,"given":"Jianning","family":"Yang","sequence":"additional","affiliation":[{"name":"Center for Image and Information Processing, Xi'an University of Posts and Telecommunications, China"}]}],"member":"320","published-online":{"date-parts":[[2023,5,16]]},"reference":[{"key":"e_1_3_2_1_1_1","volume-title":"Proceedings of the 2nd international conference on Knowledge capture. 2003: 70-77","author":"Tetsuya Nasukawa","year":"2003","unstructured":"Nasukawa Tetsuya, Yi Jeonghee. 2003. Sentiment analysis: Capturing favorability using natural language processing. Proceedings of the 2nd international conference on Knowledge capture. 2003: 70-77."},{"key":"e_1_3_2_1_2_1","first-page":"311","volume-title":"Comput. Linguist","author":"Bo Pang","year":"2009","unstructured":"Pang Bo, Lee Lillian. 2009. Opinion mining and sentiment analysis. Comput. Linguist, 2009, 35(2): 311-312."},{"key":"e_1_3_2_1_3_1","doi-asserted-by":"crossref","unstructured":"Mingqing Hu Bing Liu. 2004. Mining and summarizing customer reviews.Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining. 2004: 168-177.","DOI":"10.1145\/1014052.1014073"},{"key":"e_1_3_2_1_4_1","doi-asserted-by":"crossref","unstructured":"Wagner Joachim Arora Piyush Cortes Santiago 2014. DCU: Aspect-based Polarity Classification for SemEval Task 4.SemEval@ COLING. 2014: 223-229.","DOI":"10.3115\/v1\/S14-2036"},{"key":"e_1_3_2_1_5_1","volume-title":"Effective LSTMs for target-dependent sentiment classification. arXiv preprint arXiv:1512.01100","author":"Tang Duyu","year":"2015","unstructured":"Duyu Tang, Bing Qin, Xiaocheng Feng, 2015. Effective LSTMs for target-dependent sentiment classification. arXiv preprint arXiv:1512.01100, 2015."},{"key":"e_1_3_2_1_6_1","volume-title":"Gated neural networks for targeted sentiment analysis.Thirtieth AAAI conference on artificial intelligence","author":"Zhang Meishan","year":"2016","unstructured":"Meishan Zhang, Yue Zhang, Duy-Tin Vo. 2016. Gated neural networks for targeted sentiment analysis.Thirtieth AAAI conference on artificial intelligence. 2016."},{"key":"e_1_3_2_1_7_1","volume-title":"Vistanet: Visual aspect attention network for multimodal sentiment analysis.Proceedings of the AAAI Conference on Artificial Intelligence.","author":"Quoc-Tuan Truong","year":"2019","unstructured":"Truong Quoc-Tuan, Hady W Lauw. 2019. Vistanet: Visual aspect attention network for multimodal sentiment analysis.Proceedings of the AAAI Conference on Artificial Intelligence. 2019, 33(01): 305-312."},{"key":"e_1_3_2_1_8_1","first-page":"26","volume-title":"Knowledge-Based Systems","author":"Huang Feiran","year":"2019","unstructured":"Feiran Huang, Xiaoming Zhang, Zhonghua Zhao, 2019. Image\u2013text sentiment analysis via deep multimodal attentive fusion. Knowledge-Based Systems, 2019, 167: 26-37."},{"key":"e_1_3_2_1_9_1","first-page":"371","volume":"2019","author":"Xu Nan","unstructured":"Nan Xu, WenjiMao, Guandan Chen.2019.Multi-interactive memory network for aspect based multimodal sentiment analysis[C]\/\/Proceedings of the AAAI Conference on Artificial Intelligence. 2019, 33(01): 371-378.","journal-title":"Artificial Intelligence."},{"key":"e_1_3_2_1_10_1","volume-title":"Learning phrase representations using RNN encoder-decoder for statistical machine translation. arXiv preprint arXiv:1406.1078","author":"Cho Kyunghyun","year":"2014","unstructured":"Kyunghyun Cho, Bart van Merrienboer, Caglar Gulcehre, 2014. Learning phrase representations using RNN encoder-decoder for statistical machine translation. arXiv preprint arXiv:1406.1078, 2014."},{"key":"e_1_3_2_1_11_1","volume-title":"Analogical reasoning on chinese morphological and semantic relations. arXiv preprint arXiv:1805.06504","author":"Li Shen","year":"2018","unstructured":"Shen Li,Zhe Zhao,Renfen Hu, 2018. Analogical reasoning on chinese morphological and semantic relations. arXiv preprint arXiv:1805.06504, 2018."},{"key":"e_1_3_2_1_12_1","doi-asserted-by":"crossref","unstructured":"Yequan Wang Minlie Huang Xiaoyan Zhu 2016. Attention-based LSTM for aspect-level sentiment classification.Proceedings of the 2016 conference on empirical methods in natural language processing. 2016: 606-615.","DOI":"10.18653\/v1\/D16-1058"},{"key":"e_1_3_2_1_13_1","volume-title":"Aspect level sentiment classification with deep memory network. arXiv preprint arXiv:1605.08900","author":"Tang Duyu","year":"2016","unstructured":"Duyu Tang, Bing Qin, Ting Liu. 2016. Aspect level sentiment classification with deep memory network. arXiv preprint arXiv:1605.08900, 2016."},{"key":"e_1_3_2_1_14_1","doi-asserted-by":"crossref","unstructured":"Peng Chen Zhongqian Sun Lidong Bing 2017.Recurrent attention network on memory for aspect sentiment analysis.Proceedings of the 2017 conference on empirical methods in natural language processing. 2017: 452-461.","DOI":"10.18653\/v1\/D17-1047"},{"key":"e_1_3_2_1_15_1","volume-title":"The 41st international ACM SIGIR conference on research & development in information retrieval. 2018: 929-932","author":"Mao Wenji","year":"2018","unstructured":"Xu, Nan, Wenji Mao, and Guandan Chen. 2018. A co-memory network for multimodal sentiment analysis. The 41st international ACM SIGIR conference on research & development in information retrieval. 2018: 929-932."},{"key":"e_1_3_2_1_16_1","volume-title":"Analogical reasoning on chinese morphological and semantic relations. arXiv preprint arXiv:1805.06504","author":"Li Shen","year":"2018","unstructured":"Shen Li, Zhe Zhao, Renfen Hu, 2018. Analogical reasoning on chinese morphological and semantic relations. arXiv preprint arXiv:1805.06504, 2018."},{"key":"e_1_3_2_1_17_1","unstructured":"Kaiming He Xiangyu Zhang Shaoqing Ren 2016. Deep residual learning for image recognition[C]\/\/Proceedings of the IEEE conference on computer vision and pattern recognition. 2016: 770-778."}],"event":{"name":"AIPR 2022: 2022 5th International Conference on Artificial Intelligence and Pattern Recognition","location":"Xiamen China","acronym":"AIPR 2022"},"container-title":["Proceedings of the 2022 5th International Conference on Artificial Intelligence and Pattern Recognition"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3573942.3573971","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3573942.3573971","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T17:49:22Z","timestamp":1750182562000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3573942.3573971"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,9,23]]},"references-count":17,"alternative-id":["10.1145\/3573942.3573971","10.1145\/3573942"],"URL":"https:\/\/doi.org\/10.1145\/3573942.3573971","relation":{},"subject":[],"published":{"date-parts":[[2022,9,23]]},"assertion":[{"value":"2023-05-16","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}