{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,13]],"date-time":"2026-05-13T17:44:34Z","timestamp":1778694274409,"version":"3.51.4"},"reference-count":30,"publisher":"Springer Science and Business Media LLC","issue":"17","license":[{"start":{"date-parts":[[2024,8,7]],"date-time":"2024-08-07T00:00:00Z","timestamp":1722988800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,8,7]],"date-time":"2024-08-07T00:00:00Z","timestamp":1722988800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"DOI":"10.13039\/501100001809","name":"natural science foundation of China","doi-asserted-by":"crossref","award":["61906110"],"award-info":[{"award-number":["61906110"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/501100001809","name":"natural science foundation of China","doi-asserted-by":"crossref","award":["61906110"],"award-info":[{"award-number":["61906110"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/501100001809","name":"natural science foundation of China","doi-asserted-by":"crossref","award":["61906110"],"award-info":[{"award-number":["61906110"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]},{"name":"Shanxi province application basic research plan","award":["202303021211139"],"award-info":[{"award-number":["202303021211139"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["J Supercomput"],"published-print":{"date-parts":[[2024,11]]},"DOI":"10.1007\/s11227-024-06391-4","type":"journal-article","created":{"date-parts":[[2024,8,7]],"date-time":"2024-08-07T06:17:20Z","timestamp":1723011440000},"page":"25049-25070","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Enhancing cross-domain sentiment classification through multi-source collaborative training and selective ensemble methods"],"prefix":"10.1007","volume":"80","author":[{"given":"Chuanjun","family":"Zhao","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xinyi","family":"Yang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xuzhuang","family":"Sun","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Lihua","family":"Shen","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jing","family":"Gao","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yanjie","family":"Wang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2024,8,7]]},"reference":[{"issue":"16","key":"6391_CR1","doi-asserted-by":"publisher","first-page":"7282","DOI":"10.3390\/s23167282","volume":"23","author":"Y Kong","year":"2023","unstructured":"Kong Y, Xu Z, Mei M (2023) Cross-domain sentiment analysis based on feature projection and multi-source attention in IoT. Sensors 23(16):7282","journal-title":"Sensors"},{"key":"6391_CR2","doi-asserted-by":"publisher","first-page":"5431","DOI":"10.1007\/s00500-018-3187-9","volume":"23","author":"F Khan","year":"2019","unstructured":"Khan F, Qamar U, Bashir S (2019) Enhanced cross-domain sentiment classification utilizing a multi-source transfer learning approach. Soft Comput 23:5431\u20135442","journal-title":"Soft Comput"},{"key":"6391_CR3","doi-asserted-by":"crossref","unstructured":"Zhao C, Wang S, Li D (2014) Fuzzy sentiment membership determining for sentiment classification. In: Proceedings of the 2014 IEEE International Conference on Data Mining Workshop, pp 1191\u20131198","DOI":"10.1109\/ICDMW.2014.137"},{"key":"6391_CR4","doi-asserted-by":"crossref","unstructured":"Guo Q, Wang X, Wu Y, et al. (2020) Online knowledge distillation via collaborative learning. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp 11020\u201311029","DOI":"10.1109\/CVPR42600.2020.01103"},{"key":"6391_CR5","doi-asserted-by":"publisher","first-page":"241","DOI":"10.1007\/s11704-019-8208-z","volume":"14","author":"X Dong","year":"2020","unstructured":"Dong X, Yu Z, Cao W et al (2020) A survey on ensemble learning. Front Comput Sci 14:241\u2013258","journal-title":"Front Comput Sci"},{"key":"6391_CR6","doi-asserted-by":"publisher","first-page":"8008","DOI":"10.1109\/TIP.2021.3112012","volume":"30","author":"K Zhou","year":"2021","unstructured":"Zhou K, Yang Y, Qiao Y et al (2021) Domain adaptive ensemble learning. IEEE Trans Image Process 30:8008\u20138018","journal-title":"IEEE Trans Image Process"},{"key":"6391_CR7","doi-asserted-by":"publisher","first-page":"144957","DOI":"10.1109\/ACCESS.2019.2945340","volume":"7","author":"N Abdullah","year":"2019","unstructured":"Abdullah N, Feizollah A, Sulaiman A et al (2019) Challenges and recommended solutions in multi-source and multi-domain sentiment analysis. IEEE Access 7:144957\u2013144971","journal-title":"IEEE Access"},{"key":"6391_CR8","unstructured":"Cui X, Bollegala D (2020) Multi-source attention for unsupervised domain adaptation. In: Proceedings of the 1st Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics and the 10th International Joint Conference on Natural Language Processing, pp 873\u2013883"},{"key":"6391_CR9","doi-asserted-by":"crossref","unstructured":"Yang M, Shen Y, Chen X et al. (2020) Multi-source domain adaptation for sentiment classification with granger causal inference. In: Proceedings of the 43rd International Acm Sigir Conference on Research and Development in Information Retrieval, pp 1913\u20131916","DOI":"10.1145\/3397271.3401314"},{"key":"6391_CR10","doi-asserted-by":"crossref","unstructured":"Dai Y, Liu J, Ren X et al. (2020) Adversarial training based multi-source unsupervised domain adaptation for sentiment analysis. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol 34, pp 7618\u20137625","DOI":"10.1609\/aaai.v34i05.6262"},{"key":"6391_CR11","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2019.105254","volume":"191","author":"C Zhao","year":"2020","unstructured":"Zhao C, Wang S, Li D (2020) Multi-source domain adaptation with joint learning for cross-domain sentiment classification. Knowl-Based Syst 191:105254","journal-title":"Knowl-Based Syst"},{"key":"6391_CR12","doi-asserted-by":"publisher","first-page":"281","DOI":"10.1016\/j.ins.2021.07.001","volume":"578","author":"C Zhao","year":"2021","unstructured":"Zhao C, Wang S, Li D et al (2021) Cross-domain sentiment classification via parameter transferring and attention sharing mechanism. Inf Sci 578:281\u2013296","journal-title":"Inf Sci"},{"issue":"9","key":"6391_CR13","first-page":"1380","volume":"63","author":"Z Chuanjun","year":"2023","unstructured":"Chuanjun Z, Meiling W, Lihua S et al (2023) Cross-domain sentiment classification based on syntactic structure transfer and domain fusion. J Tsinghua Univ (Sci Technol) 63(9):1380\u20131389","journal-title":"J Tsinghua Univ (Sci Technol)"},{"issue":"4","key":"6391_CR14","doi-asserted-by":"publisher","first-page":"911","DOI":"10.1080\/09540091.2021.1912711","volume":"33","author":"Z Cao","year":"2021","unstructured":"Cao Z et al (2021) Deep transfer learning mechanism for fine-grained cross-domain sentiment classification. Connect Sci 33(4):911\u2013928","journal-title":"Connect Sci"},{"issue":"16","key":"6391_CR15","doi-asserted-by":"publisher","first-page":"7282","DOI":"10.3390\/s23167282","volume":"23","author":"Y Kong","year":"2023","unstructured":"Kong Y, Xu Z, Mei M (2023) Cross-domain sentiment analysis based on feature projection and multi-source attention in IoT. Sensors 23(16):7282","journal-title":"Sensors"},{"key":"6391_CR16","doi-asserted-by":"crossref","unstructured":"Isobe T, Jia X, Chen S et al. (2021) Multi-target domain adaptation with collaborative consistency learning. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp 8187\u20138196","DOI":"10.1109\/CVPR46437.2021.00809"},{"key":"6391_CR17","doi-asserted-by":"crossref","unstructured":"He J, Jia X, Chen S et al. (2021) Multi-source domain adaptation with collaborative learning for semantic segmentation. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp 11008\u201311017","DOI":"10.1109\/CVPR46437.2021.01086"},{"issue":"5","key":"6391_CR18","doi-asserted-by":"publisher","first-page":"2202","DOI":"10.1109\/TCSVT.2022.3219893","volume":"33","author":"Y Wei","year":"2023","unstructured":"Wei Y, Yang L, Han Y et al (2023) Multi-source collaborative contrastive learning for decentralized domain adaptation. IEEE Trans Circuits Syst Video Technol 33(5):2202\u20132216","journal-title":"IEEE Trans Circuits Syst Video Technol"},{"issue":"12","key":"6391_CR19","doi-asserted-by":"publisher","first-page":"1780","DOI":"10.1631\/FITEE.2200284","volume":"23","author":"Y Wei","year":"2022","unstructured":"Wei Y, Han Y (2022) Dual collaboration for decentralized multi-source domain adaptation. Front Inf Technol ElectrEng 23(12):1780\u20131794","journal-title":"Front Inf Technol ElectrEng"},{"key":"6391_CR20","doi-asserted-by":"publisher","first-page":"166488","DOI":"10.1109\/ACCESS.2021.3136567","volume":"9","author":"B Ngo","year":"2021","unstructured":"Ngo B, Kim J, Chae Y et al (2021) Multi-view collaborative learning for semi-supervised domain adaptation. IEEE Access 9:166488\u2013166501","journal-title":"IEEE Access"},{"issue":"2","key":"6391_CR21","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s11227-021-03966-3","volume":"78","author":"A Chen","year":"2022","unstructured":"Chen A, Yang P, Cheng P (2022) ACTSSD: Social spammer detection based on active learning and co-training. J Supercomput 78(2):1\u201328","journal-title":"J Supercomput"},{"key":"6391_CR22","doi-asserted-by":"crossref","unstructured":"Amosy O, Chechik G (2022) Coupled training for multi-source domain adaptation. In: Proceedings of the IEEE\/CVF Winter Conference on Applications of Computer Vision, pp 420\u2013429","DOI":"10.1109\/WACV51458.2022.00114"},{"key":"6391_CR23","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2021.115819","volume":"187","author":"J Kazmaier","year":"2022","unstructured":"Kazmaier J, Van Vuuren J (2022) The power of ensemble learning in sentiment analysis. Expert Syst Appl 187:115819","journal-title":"Expert Syst Appl"},{"key":"6391_CR24","unstructured":"Zhou Z-H, Wu J-X, Jiang Y, Chen S-F (2001) Genetic algorithm based selective neural network ensemble. In: Proceedings of the 17th International Joint Conference on Artificial intelligence, Volume 2, pp 797\u2013802. Morgan Kaufmann Publishers Inc"},{"key":"6391_CR25","doi-asserted-by":"publisher","first-page":"1185","DOI":"10.1007\/s12559-020-09792-8","volume":"13","author":"Y Dai","year":"2021","unstructured":"Dai Y, Liu J, Zhang J et al (2021) Unsupervised sentiment analysis by transferring multi-source knowledge. Cogn Comput 13:1185\u20131197","journal-title":"Cogn Comput"},{"issue":"6","key":"6391_CR26","doi-asserted-by":"publisher","DOI":"10.1016\/j.ipm.2021.102691","volume":"58","author":"X Yu","year":"2021","unstructured":"Yu X, Peng Q, Xu L et al (2021) A selective ensemble learning based two-sided cross-domain collaborative filtering algorithm. Inf Process Manag 58(6):102691","journal-title":"Inf Process Manag"},{"key":"6391_CR27","doi-asserted-by":"publisher","first-page":"33822","DOI":"10.1109\/ACCESS.2019.2903550","volume":"7","author":"X Zhang","year":"2019","unstructured":"Zhang X, Yan F, Zhuang Y et al (2019) Using an ensemble of incrementally fine-tuned CNNs for cross-domain object category recognition. IEEE Access 7:33822\u201333833","journal-title":"IEEE Access"},{"issue":"2","key":"6391_CR28","first-page":"37","volume":"1","author":"G Jenset","year":"2019","unstructured":"Jenset G, McGillivray B (2019) Enhancing domain-specific supervised natural language intent classification with a top\u2013down selective ensemble model. Mach Learn Knowl Extr 1(2):37","journal-title":"Mach Learn Knowl Extr"},{"issue":"4","key":"6391_CR29","doi-asserted-by":"publisher","first-page":"5394","DOI":"10.1007\/s11227-021-04084-w","volume":"78","author":"Z Yan","year":"2022","unstructured":"Yan Z, Hongle D, Gang K et al (2022) Dynamic weighted selective ensemble learning algorithm for imbalanced data streams. J Supercomput 78(4):5394\u20135419","journal-title":"J Supercomput"},{"key":"6391_CR30","doi-asserted-by":"publisher","first-page":"2875","DOI":"10.1007\/s11227-020-03374-z","volume":"77","author":"H Du","year":"2021","unstructured":"Du H, Zhang Y (2021) Network anomaly detection based on selective ensemble algorithm. J Supercomput 77:2875\u20132896","journal-title":"J Supercomput"}],"container-title":["The Journal of Supercomputing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11227-024-06391-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11227-024-06391-4\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11227-024-06391-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,9,5]],"date-time":"2024-09-05T15:30:33Z","timestamp":1725550233000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11227-024-06391-4"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,8,7]]},"references-count":30,"journal-issue":{"issue":"17","published-print":{"date-parts":[[2024,11]]}},"alternative-id":["6391"],"URL":"https:\/\/doi.org\/10.1007\/s11227-024-06391-4","relation":{},"ISSN":["0920-8542","1573-0484"],"issn-type":[{"value":"0920-8542","type":"print"},{"value":"1573-0484","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,8,7]]},"assertion":[{"value":"29 July 2024","order":1,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"7 August 2024","order":2,"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 that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}