{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,28]],"date-time":"2026-02-28T20:15:36Z","timestamp":1772309736991,"version":"3.50.1"},"reference-count":80,"publisher":"Springer Science and Business Media LLC","issue":"3","license":[{"start":{"date-parts":[[2022,12,26]],"date-time":"2022-12-26T00:00:00Z","timestamp":1672012800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2022,12,26]],"date-time":"2022-12-26T00:00:00Z","timestamp":1672012800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61271361"],"award-info":[{"award-number":["61271361"]}],"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":["61761046"],"award-info":[{"award-number":["61761046"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Key Program of the Applied Basic Research Programs of Yunnan","award":["202001BB050043"],"award-info":[{"award-number":["202001BB050043"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Neural Process Lett"],"published-print":{"date-parts":[[2023,6]]},"DOI":"10.1007\/s11063-022-11124-w","type":"journal-article","created":{"date-parts":[[2022,12,26]],"date-time":"2022-12-26T08:02:35Z","timestamp":1672041755000},"page":"2103-2125","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":16,"title":["Image\u2013Text Sentiment Analysis Via Context Guided Adaptive Fine-Tuning Transformer"],"prefix":"10.1007","volume":"55","author":[{"given":"Xingwang","family":"Xiao","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yuanyuan","family":"Pu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhengpeng","family":"Zhao","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Rencan","family":"Nie","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Dan","family":"Xu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Wenhua","family":"Qian","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hao","family":"Wu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2022,12,26]]},"reference":[{"issue":"4","key":"11124_CR1","doi-asserted-by":"publisher","first-page":"1253","DOI":"10.1002\/widm.1253","volume":"8","author":"L Zhang","year":"2018","unstructured":"Zhang L, Wang S, Liu B (2018) Deep learning for sentiment analysis: a survey. Wiley Interdiscip Rev Data Min Knowl Discov 8(4):1253","journal-title":"Wiley Interdiscip Rev Data Min Knowl Discov"},{"issue":"2","key":"11124_CR2","doi-asserted-by":"publisher","first-page":"617","DOI":"10.1007\/s10115-018-1236-4","volume":"60","author":"L Yue","year":"2019","unstructured":"Yue L, Chen W, Li X, Zuo W, Yin M (2019) A survey of sentiment analysis in social media. Knowl Inf Syst 60(2):617\u2013663","journal-title":"Knowl Inf Syst"},{"key":"11124_CR3","unstructured":"Cui H, Mittal V, Datar M (2006) Comparative experiments on sentiment classification for online product reviews. In: Proceedings of the 21st national conference on artificial intelligence, vol 2, pp 1265\u20131270"},{"key":"11124_CR4","unstructured":"Wei W, Gulla JA (2010) Sentiment learning on product reviews via sentiment ontology tree. In: Proceedings of the 48th annual meeting of the association for computational linguistics, pp 404\u2013413"},{"key":"11124_CR5","doi-asserted-by":"crossref","unstructured":"Tang D, Qin B, Liu T (2015) Learning semantic representations of users and products for document level sentiment classification. In: Proceedings of the 53rd annual meeting of the association for computational linguistics and the 7th international joint conference on natural language processing (volume 1: long papers), pp 1014\u20131023","DOI":"10.3115\/v1\/P15-1098"},{"key":"11124_CR6","doi-asserted-by":"publisher","first-page":"14","DOI":"10.1016\/j.knosys.2014.04.022","volume":"69","author":"X Li","year":"2014","unstructured":"Li X, Xie H, Chen L, Wang J, Deng X (2014) News impact on stock price return via sentiment analysis. Knowl Based Syst 69:14\u201323","journal-title":"Knowl Based Syst"},{"key":"11124_CR7","doi-asserted-by":"crossref","unstructured":"Nguyen TH, Shirai K (2015) Topic modeling based sentiment analysis on social media for stock market prediction. In: Proceedings of the 53rd annual meeting of the association for computational linguistics and the 7th international joint conference on natural language processing (volume 1: long papers), pp 1354\u20131364","DOI":"10.3115\/v1\/P15-1131"},{"issue":"4","key":"11124_CR8","doi-asserted-by":"publisher","first-page":"1245","DOI":"10.1016\/j.ipm.2019.02.018","volume":"56","author":"A Abdi","year":"2019","unstructured":"Abdi A, Shamsuddin SM, Hasan S, Piran J (2019) Deep learning-based sentiment classification of evaluative text based on multi-feature fusion. Inf Process Manag 56(4):1245\u20131259","journal-title":"Inf Process Manag"},{"key":"11124_CR9","doi-asserted-by":"crossref","unstructured":"Yue Y (2019) Scale adaptation of text sentiment analysis algorithm in big data environment: Twitter as data source. In: International conference on big data analytics for cyber-physical-systems. Springer, pp 629\u2013634","DOI":"10.1007\/978-981-15-2568-1_86"},{"key":"11124_CR10","doi-asserted-by":"crossref","unstructured":"Li G, Zheng Q, Zhang L, Guo S, Niu L (2020) Sentiment information based model for Chinese text sentiment analysis. In: 2020 IEEE 3rd international conference on automation, electronics and electrical engineering (AUTEEE). IEEE, pp 366\u2013371","DOI":"10.1109\/AUTEEE50969.2020.9315668"},{"key":"11124_CR11","doi-asserted-by":"crossref","unstructured":"Kosti R, Alvarez JM, Recasens A, Lapedriza A (2017) Emotion recognition in context. In: 2017 IEEE conference on computer vision and pattern recognition (CVPR), pp 1960\u20131968","DOI":"10.1109\/CVPR.2017.212"},{"key":"11124_CR12","doi-asserted-by":"publisher","first-page":"429","DOI":"10.1016\/j.neucom.2018.12.053","volume":"333","author":"T Rao","year":"2019","unstructured":"Rao T, Li X, Zhang H, Xu M (2019) Multi-level region-based convolutional neural network for image emotion classification. Neurocomputing 333:429\u2013439","journal-title":"Neurocomputing"},{"key":"11124_CR13","doi-asserted-by":"crossref","unstructured":"Mittal N, Sharma D, Joshi ML (2018) Image sentiment analysis using deep learning. In: 2018 IEEE\/WIC\/ACM international conference on web intelligence (WI), pp 684\u2013687","DOI":"10.1109\/WI.2018.00-11"},{"issue":"7","key":"11124_CR14","doi-asserted-by":"publisher","first-page":"66","DOI":"10.3390\/electronics8070783","volume":"8","author":"E Ragusa","year":"2019","unstructured":"Ragusa E, Cambria E, Zunino R, Gastaldo P (2019) A survey on deep learning in image polarity detection: balancing generalization performances and computational costs. Electronics 8(7):66","journal-title":"Electronics"},{"issue":"2","key":"11124_CR15","first-page":"38","volume":"10","author":"R Kaur","year":"2019","unstructured":"Kaur R, Kautish S (2019) Multimodal sentiment analysis: a survey and comparison. Int J Serv Sci Manag Eng Technol 10(2):38\u201358","journal-title":"Int J Serv Sci Manag Eng Technol"},{"key":"11124_CR16","doi-asserted-by":"publisher","first-page":"3","DOI":"10.1016\/j.imavis.2017.08.003","volume":"65","author":"M Soleymani","year":"2017","unstructured":"Soleymani M, Garcia D, Jou B, Schuller B, Chang S-F, Pantic M (2017) A survey of multimodal sentiment analysis. Image Vis Comput 65:3\u201314","journal-title":"Image Vis Comput"},{"key":"11124_CR17","unstructured":"Bengio Y (2012) Deep learning of representations for unsupervised and transfer learning. In: Proceedings of ICML workshop on unsupervised and transfer learning. JMLR workshop and conference proceedings, pp 17\u201336"},{"issue":"1","key":"11124_CR18","doi-asserted-by":"publisher","first-page":"43","DOI":"10.1109\/JPROC.2020.3004555","volume":"109","author":"F Zhuang","year":"2021","unstructured":"Zhuang F, Qi Z, Duan K, Xi D, Zhu Y, Zhu H, Xiong H, He Q (2021) A comprehensive survey on transfer learning. Proc IEEE 109(1):43\u201376","journal-title":"Proc IEEE"},{"key":"11124_CR19","doi-asserted-by":"publisher","first-page":"85401","DOI":"10.1109\/ACCESS.2019.2925059","volume":"7","author":"R Liu","year":"2019","unstructured":"Liu R, Shi Y, Ji C, Jia M (2019) A survey of sentiment analysis based on transfer learning. IEEE Access 7:85401\u201385412","journal-title":"IEEE Access"},{"issue":"6","key":"11124_CR20","doi-asserted-by":"publisher","first-page":"6939","DOI":"10.1007\/s11042-018-6445-z","volume":"78","author":"Z Li","year":"2019","unstructured":"Li Z, Fan Y, Jiang B, Lei T, Liu W (2019) A survey on sentiment analysis and opinion mining for social multimedia. Multimed Tools Appl 78(6):6939\u20136967","journal-title":"Multimed Tools Appl"},{"key":"11124_CR21","doi-asserted-by":"crossref","unstructured":"Deng J, Dong W, Socher R, Li L-J, Li K, Fei-Fei L (2009) Imagenet: a large-scale hierarchical image database. In: 2009 IEEE conference on computer vision and pattern recognition (CVPR), pp 248\u2013255","DOI":"10.1109\/CVPR.2009.5206848"},{"key":"11124_CR22","doi-asserted-by":"crossref","unstructured":"Hu A, Flaxman S (2018) Multimodal sentiment analysis to explore the structure of emotions. In: Proceedings of the 24th ACM SIGKDD international conference on knowledge discovery and data mining, pp 350\u2013358","DOI":"10.1145\/3219819.3219853"},{"key":"11124_CR23","doi-asserted-by":"crossref","unstructured":"Thuseethan S, Janarthan S, Rajasegarar S, Kumari P, Yearwood J (2020) Multimodal deep learning framework for sentiment analysis from text-image web data. In: 2020 IEEE\/WIC\/ACM international joint conference on web intelligence and intelligent agent technology (WI-IAT), pp 267\u2013274","DOI":"10.1109\/WIIAT50758.2020.00039"},{"key":"11124_CR24","doi-asserted-by":"crossref","unstructured":"Basu P, Tiwari S, Mohanty J, Karmakar S (2020) Multimodal sentiment analysis of metoo tweets using focal loss (grand challenge). In: 2020 IEEE sixth international conference on multimedia big data (BigMM), pp 461\u2013465","DOI":"10.1109\/BigMM50055.2020.00076"},{"key":"11124_CR25","doi-asserted-by":"publisher","first-page":"26","DOI":"10.1016\/j.knosys.2019.01.019","volume":"167","author":"F Huang","year":"2019","unstructured":"Huang F, Zhang X, Zhao Z, Xu J, Li Z (2019) Image\u2013text sentiment analysis via deep multimodal attentive fusion. Knowl Based Syst 167:26\u201337","journal-title":"Knowl Based Syst"},{"key":"11124_CR26","doi-asserted-by":"crossref","unstructured":"Xu N, Mao W (2017) Multisentinet: a deep semantic network for multimodal sentiment analysis. In: Proceedings of the 2017 ACM on conference on information and knowledge management, pp 2399\u20132402","DOI":"10.1145\/3132847.3133142"},{"key":"11124_CR27","doi-asserted-by":"publisher","first-page":"4014","DOI":"10.1109\/TMM.2020.3035277","volume":"23","author":"X Yang","year":"2021","unstructured":"Yang X, Feng S, Wang D, Zhang Y (2021) Image\u2013text multimodal emotion classification via multi-view attentional network. IEEE Trans Multimed 23:4014\u20134026","journal-title":"IEEE Trans Multimed"},{"key":"11124_CR28","unstructured":"Mikolov T, Chen K, Corrado G, Dean J (2013) Efficient estimation of word representations in vector space. arXiv preprint arXiv:1301.3781"},{"key":"11124_CR29","doi-asserted-by":"crossref","unstructured":"Pennington J, Socher R, Manning C (2014) GloVe: global vectors for word representation. In: Proceedings of the 2014 conference on empirical methods in natural language processing (EMNLP), pp 1532\u20131543","DOI":"10.3115\/v1\/D14-1162"},{"key":"11124_CR30","unstructured":"Devlin J, Chang M-W, 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"},{"key":"11124_CR31","doi-asserted-by":"crossref","unstructured":"Sun Y, Wang S, Li Y, Feng S, Tian H, Wu H, Wang H (2020) Ernie 2.0: a continual pre-training framework for language understanding. In: Proceedings of the AAAI conference on artificial intelligence, vol 34, pp 8968\u20138975","DOI":"10.1609\/aaai.v34i05.6428"},{"key":"11124_CR32","doi-asserted-by":"crossref","unstructured":"He K, Zhang X, Ren S, Sun J (2016) Deep residual learning for image recognition. In: 2016 IEEE conference on computer vision and pattern recognition (CVPR), pp 770\u2013778","DOI":"10.1109\/CVPR.2016.90"},{"key":"11124_CR33","unstructured":"Kiela D, Bhooshan S, Firooz H, Perez E, Testuggine D (2019) Supervised multimodal bitransformers for classifying images and text. arXiv preprint arXiv:1909.02950"},{"key":"11124_CR34","unstructured":"Yosinski J, Clune J, Bengio Y, Lipson H (2014) How transferable are features in deep neural networks? In: Proceedings of the 27th international conference on neural information processing systems\u2014volume 2, pp 3320\u20133328"},{"issue":"5","key":"11124_CR35","doi-asserted-by":"publisher","first-page":"1299","DOI":"10.1109\/TMI.2016.2535302","volume":"35","author":"N Tajbakhsh","year":"2016","unstructured":"Tajbakhsh N, Shin JY, Gurudu SR, Hurst RT, Kendall CB, Gotway MB, Liang J (2016) Convolutional neural networks for medical image analysis: full training or fine tuning? IEEE Trans Med Imaging 35(5):1299\u20131312","journal-title":"IEEE Trans Med Imaging"},{"issue":"9","key":"11124_CR36","doi-asserted-by":"publisher","first-page":"1790","DOI":"10.1109\/TPAMI.2015.2500224","volume":"38","author":"H Azizpour","year":"2016","unstructured":"Azizpour H, Razavian AS, Sullivan J, Maki A, Carlsson S (2016) Factors of transferability for a generic convnet representation. IEEE Trans Pattern Anal Mach Intell 38(9):1790\u20131802","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"11124_CR37","doi-asserted-by":"crossref","unstructured":"Guo Y, Shi H, Kumar A, Grauman K, Rosing T, Feris R (2019) Spottune: transfer learning through adaptive fine-tuning. In: 2019 IEEE\/CVF conference on computer vision and pattern recognition (CVPR), pp 4800\u20134809","DOI":"10.1109\/CVPR.2019.00494"},{"key":"11124_CR38","doi-asserted-by":"crossref","unstructured":"Yuan L, Chen Y, Wang T, Yu W, Shi Y, Jiang Z-H, Tay FEH, Feng J, Yan S (2021) Tokens-to-token vit: training vision transformers from scratch on imagenet. In: Proceedings of the IEEE\/CVF international conference on computer vision (ICCV), pp 558\u2013567","DOI":"10.1109\/ICCV48922.2021.00060"},{"key":"11124_CR39","doi-asserted-by":"crossref","unstructured":"Niu T, Zhu S, Pang L, El\u00a0Saddik A (2016) Sentiment analysis on multi-view social data. In: International conference on multimedia modeling. Springer, pp 15\u201327","DOI":"10.1007\/978-3-319-27674-8_2"},{"key":"11124_CR40","first-page":"1","volume":"66","author":"L Wu","year":"2019","unstructured":"Wu L, Qi M, Jian M, Zhang H (2019) Visual sentiment analysis by combining global and local information. Neural Process Lett 66:1\u201313","journal-title":"Neural Process Lett"},{"key":"11124_CR41","doi-asserted-by":"crossref","unstructured":"Ben\u00a0Ahmed K, Bouhorma M, Ben\u00a0Ahmed M, Radenski A (2016) Visual sentiment prediction with transfer learning and big data analytics for smart cities. In: 2016 4th IEEE international colloquium on information science and technology (CiSt), pp 800\u2013805","DOI":"10.1109\/CIST.2016.7804997"},{"key":"11124_CR42","first-page":"1","volume":"66","author":"W Li","year":"2021","unstructured":"Li W, Dong X, Wang Y (2021) Human emotion recognition with relational region-level analysis. IEEE Trans Aff Comput 66:1\u20131","journal-title":"IEEE Trans Aff Comput"},{"issue":"6","key":"11124_CR43","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(6):1137\u20131149","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"issue":"10","key":"11124_CR44","doi-asserted-by":"publisher","first-page":"296","DOI":"10.1167\/17.10.296","volume":"17","author":"B Zhou","year":"2017","unstructured":"Zhou B, Lapedriza A, Torralba A, Oliva A (2017) Places: an image database for deep scene understanding. J Vis 17(10):296\u2013296","journal-title":"J Vis"},{"key":"11124_CR45","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2019.105245","volume":"191","author":"J Zhang","year":"2020","unstructured":"Zhang J, Chen M, Sun H, Li D, Wang Z (2020) Object semantics sentiment correlation analysis enhanced image sentiment classification. Knowl Based Syst 191:105245","journal-title":"Knowl Based Syst"},{"key":"11124_CR46","first-page":"66","volume":"6","author":"J Zhang","year":"2021","unstructured":"Zhang J, Liu X, Chen M, Ye Q, Wang Z (2021) Image sentiment classification via multi-level sentiment region correlation analysis. Neurocomputing 6:66","journal-title":"Neurocomputing"},{"issue":"43","key":"11124_CR47","doi-asserted-by":"publisher","first-page":"32389","DOI":"10.1007\/s11042-020-09632-9","volume":"79","author":"S Sagnika","year":"2020","unstructured":"Sagnika S, Mishra BSP, Meher SK (2020) Improved method of word embedding for efficient analysis of human sentiments. Multimed Tools Appl 79(43):32389\u201332413","journal-title":"Multimed Tools Appl"},{"key":"11124_CR48","first-page":"1","volume":"66","author":"P Demotte","year":"2021","unstructured":"Demotte P, Wijegunarathna K, Meedeniya D, Perera I (2021) Enhanced sentiment extraction architecture for social media content analysis using capsule networks. Multimed Tools Appl 66:1\u201326","journal-title":"Multimed Tools Appl"},{"key":"11124_CR49","unstructured":"Vaswani A, Shazeer N, Parmar N, Uszkoreit J, Jones L, Gomez AN, Kaiser L, Polosukhin I (2017) Attention is all you need. In: Proceedings of the 31st international conference on neural information processing systems. Curran Associates Inc., Red Hook, pp 6000\u20136010"},{"issue":"6","key":"11124_CR50","doi-asserted-by":"publisher","first-page":"4207","DOI":"10.1007\/s11063-021-10596-6","volume":"53","author":"A Kumar","year":"2021","unstructured":"Kumar A, Gupta P, Balan R, Neti LBM, Malapati A (2021) Bert based semi-supervised hybrid approach for aspect and sentiment classification. Neural Process Lett 53(6):4207\u20134224","journal-title":"Neural Process Lett"},{"issue":"4","key":"11124_CR51","first-page":"3831","volume":"53","author":"F Mehrdad","year":"2021","unstructured":"Mehrdad F, Mohammad G, Marzieh F, Mohammad M (2021) Parsbert: transformer-based model for Persian language understanding. Neural Process Lett 53(4):3831\u20133847","journal-title":"Neural Process Lett"},{"key":"11124_CR52","first-page":"1","volume":"66","author":"K Wang","year":"2022","unstructured":"Wang K, Wan X (2022) Counterfactual representation augmentation for cross-domain sentiment analysis. IEEE Trans Aff Comput 66:1\u20131","journal-title":"IEEE Trans Aff Comput"},{"key":"11124_CR53","doi-asserted-by":"crossref","unstructured":"Guo H, Chi C, Zhan X (2021) Ernie-bilstm based Chinese text sentiment classification method. In: 2021 International conference on computer engineering and application (ICCEA), pp 84\u201388","DOI":"10.1109\/ICCEA53728.2021.00024"},{"key":"11124_CR54","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2021.107643","volume":"235","author":"B Liang","year":"2022","unstructured":"Liang B, Su H, Gui L, Cambria E, Xu R (2022) Aspect-based sentiment analysis via affective knowledge enhanced graph convolutional networks. Knowl Based Syst 235:107643","journal-title":"Knowl Based Syst"},{"key":"11124_CR55","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"},{"issue":"3","key":"11124_CR56","doi-asserted-by":"publisher","first-page":"38","DOI":"10.1109\/MIS.2019.2904691","volume":"34","author":"N Majumder","year":"2019","unstructured":"Majumder N, Poria S, Peng H, Chhaya N, Cambria E, Gelbukh A (2019) Sentiment and sarcasm classification with multitask learning. IEEE Intell Syst 34(3):38\u201343","journal-title":"IEEE Intell Syst"},{"key":"11124_CR57","doi-asserted-by":"crossref","unstructured":"Xu N (2017) Analyzing multimodal public sentiment based on hierarchical semantic attentional network. In: 2017 IEEE international conference on intelligence and security informatics (ISI), pp 152\u2013154","DOI":"10.1109\/ISI.2017.8004895"},{"key":"11124_CR58","doi-asserted-by":"crossref","unstructured":"Szegedy C, Liu W, Jia Y, Sermanet P, Reed S, Anguelov D, Erhan D, Vanhoucke V, Rabinovich A (2015) Going deeper with convolutions. In: 2015 IEEE conference on computer vision and pattern recognition (CVPR), pp 1\u20139","DOI":"10.1109\/CVPR.2015.7298594"},{"key":"11124_CR59","doi-asserted-by":"publisher","first-page":"140426","DOI":"10.1109\/ACCESS.2020.3006563","volume":"8","author":"S Seo","year":"2020","unstructured":"Seo S, Na S, Kim J (2020) Hmtl: heterogeneous modality transfer learning for audio-visual sentiment analysis. IEEE Access 8:140426\u2013140437","journal-title":"IEEE Access"},{"key":"11124_CR60","unstructured":"Simonyan K, Zisserman A. Very deep convolutional networks for large-scale image recognition"},{"key":"11124_CR61","doi-asserted-by":"crossref","unstructured":"He K, Zhang X, Ren S, Sun J (2015) Delving deep into rectifiers: Surpassing human-level performance on imagenet classification. In: 2015 IEEE international conference on computer vision (ICCV), pp 1026\u20131034","DOI":"10.1109\/ICCV.2015.123"},{"key":"11124_CR62","unstructured":"Zhou B, Lapedriza A, Xiao J, Torralba A, Oliva A (2014) Learning deep features for scene recognition using places database. In: Proceedings of the 27th international conference on neural information processing systems, vol 1, pp 487\u2013495"},{"key":"11124_CR63","doi-asserted-by":"crossref","unstructured":"Xu N, Mao W, Chen G (2018) A co-memory network for multimodal sentiment analysis. In: The 41st international ACM SIGIR conference on research and development in information retrieval, pp 929\u2013932","DOI":"10.1145\/3209978.3210093"},{"key":"11124_CR64","doi-asserted-by":"crossref","unstructured":"Szegedy C, Vanhoucke V, Ioffe S, Shlens J, Wojna Z (2016) Rethinking the inception architecture for computer vision. In: 2016 IEEE conference on computer vision and pattern recognition (CVPR), pp 2818\u20132826","DOI":"10.1109\/CVPR.2016.308"},{"issue":"6","key":"11124_CR65","doi-asserted-by":"publisher","first-page":"1452","DOI":"10.1109\/TPAMI.2017.2723009","volume":"40","author":"B Zhou","year":"2018","unstructured":"Zhou B, Lapedriza A, Khosla A, Oliva A, Torralba A (2018) Places: a 10 million image database for scene recognition. IEEE Trans Pattern Anal Mach Intell 40(6):1452\u20131464","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"11124_CR66","first-page":"1","volume":"66","author":"J Yu","year":"2022","unstructured":"Yu J, Chen K, Xia R (2022) Hierarchical interactive multimodal transformer for aspect-based multimodal sentiment analysis. IEEE Trans Aff Comput 66:1\u20131","journal-title":"IEEE Trans Aff Comput"},{"key":"11124_CR67","doi-asserted-by":"crossref","unstructured":"Yang X, Feng S, Zhang Y, Wang D (2021) Multimodal sentiment detection based on multi-channel graph neural networks. 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 328\u2013339","DOI":"10.18653\/v1\/2021.acl-long.28"},{"key":"11124_CR68","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s10489-021-02936-9","volume":"52","author":"W Liao","year":"2022","unstructured":"Liao W, Zeng B, Liu J, Wei P, Fang J (2022) Image\u2013text interaction graph neural network for image\u2013text sentiment analysis. Appl Intell 52:1\u201315","journal-title":"Appl Intell"},{"key":"11124_CR69","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1109\/TMM.2022.3231108","volume":"66","author":"T Zhu","year":"2022","unstructured":"Zhu T, Li L, Yang J, Zhao S, Liu H, Qian J (2022) Multimodal sentiment analysis with image\u2013text interaction network. IEEE Trans Multimed 66:1\u20131","journal-title":"IEEE Trans Multimed"},{"key":"11124_CR70","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":"11124_CR71","doi-asserted-by":"crossref","unstructured":"Cambria E, Howard N, Hsu J, Hussain A (2013) Sentic blending: scalable multimodal fusion for the continuous interpretation of semantics and sentics. In: 2013 IEEE symposium on computational intelligence for human-like intelligence (CIHLI), pp 108\u2013117","DOI":"10.1109\/CIHLI.2013.6613272"},{"key":"11124_CR72","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":"11124_CR73","doi-asserted-by":"publisher","first-page":"2015","DOI":"10.1109\/TASLP.2022.3178204","volume":"30","author":"B Yang","year":"2022","unstructured":"Yang B, Wu L, Zhu J, Shao B, Lin X, Liu T-Y (2022) Multimodal sentiment analysis with two-phase multi-task learning. IEEE\/ACM Trans Audio Speech Lang Process 30:2015\u20132024","journal-title":"IEEE\/ACM Trans Audio Speech Lang Process"},{"key":"11124_CR74","doi-asserted-by":"crossref","unstructured":"Jiang D, Wei R, Liu H, Wen J, Tu G, Zheng L, Cambria E (2021) A multitask learning framework for multimodal sentiment analysis. In: 2021 International conference on data mining workshops (ICDMW), pp 151\u2013157","DOI":"10.1109\/ICDMW53433.2021.00025"},{"key":"11124_CR75","unstructured":"Dosovitskiy A, Beyer L, Kolesnikov A, Weissenborn D, Zhai X, Unterthiner T, Dehghani M, Minderer M, Heigold G, Gelly S, Uszkoreit J, Houlsby N (2021) An image is worth 16x16 words: transformers for image recognition at scale. In: International conference on learning representations"},{"key":"11124_CR76","doi-asserted-by":"crossref","unstructured":"Liu Z, Lin Y, Cao Y, Hu H, Wei Y, Zhang Z, Lin S, Guo B (2021) Swin transformer: hierarchical vision transformer using shifted windows. In: 2021 IEEE\/CVF international conference on computer vision (ICCV), pp 9992\u201310002","DOI":"10.1109\/ICCV48922.2021.00986"},{"key":"11124_CR77","doi-asserted-by":"crossref","unstructured":"Wu K, Peng H, Chen M, Fu J, Chao H (2021) Rethinking and improving relative position encoding for vision transformer. In: Proceedings of the IEEE\/CVF international conference on computer vision (ICCV), pp 10033\u201310041","DOI":"10.1109\/ICCV48922.2021.00988"},{"key":"11124_CR78","doi-asserted-by":"crossref","unstructured":"Yang J, Sun M, Sun X (2017) Learning visual sentiment distributions via augmented conditional probability neural network. In: Proceedings of the thirty-first AAAI conference on artificial intelligence, pp 224\u2013230","DOI":"10.1609\/aaai.v31i1.10485"},{"key":"11124_CR79","doi-asserted-by":"crossref","unstructured":"Borth D, Ji R, Chen T, Breuel T, Chang S-F (2013) Large-scale visual sentiment ontology and detectors using adjective noun pairs. In: Proceedings of the 21st ACM international conference on multimedia, pp 223\u2013232","DOI":"10.1145\/2502081.2502282"},{"key":"11124_CR80","doi-asserted-by":"crossref","unstructured":"Machajdik J, Hanbury A (2010) Affective image classification using features inspired by psychology and art theory. In: Proceedings of the 18th ACM international conference on multimedia, pp 83\u201392","DOI":"10.1145\/1873951.1873965"}],"container-title":["Neural Processing Letters"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11063-022-11124-w.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11063-022-11124-w\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11063-022-11124-w.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,12,3]],"date-time":"2023-12-03T14:26:30Z","timestamp":1701613590000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11063-022-11124-w"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,12,26]]},"references-count":80,"journal-issue":{"issue":"3","published-print":{"date-parts":[[2023,6]]}},"alternative-id":["11124"],"URL":"https:\/\/doi.org\/10.1007\/s11063-022-11124-w","relation":{},"ISSN":["1370-4621","1573-773X"],"issn-type":[{"value":"1370-4621","type":"print"},{"value":"1573-773X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,12,26]]},"assertion":[{"value":"11 December 2022","order":1,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"26 December 2022","order":2,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}]}}