Multi-source domain adaptation method for textual emotion classification using deep and broad learning

doi:10.1016/j.knosys.2022.110173

CORC > 自动化研究所 > 中国科学院自动化研究所 > 复杂系统认知与决策实验室

	Multi-source domain adaptation method for textual emotion classification using deep and broad learning
	Peng, Sancheng 6; Zeng, Rong 5; Cao, Lihong 6; Yang, Aimin 4; Niu, Jianwei 3; Zong, Chengqing 2; Zhou, Guodong 1
刊名	KNOWLEDGE-BASED SYSTEMS
	2023-01-25
卷号	260 页码:9
关键词	Multi-domain Emotion classification BERT Broad learning Bi-LSTM
ISSN号	0950-7051
DOI	10.1016/j.knosys.2022.110173
通讯作者	Cao, Lihong(201610130@oamail.gdufs.edu.cn)
英文摘要	Existing domain adaptation methods for classifying textual emotions have the propensity to focus on single-source domain exploration rather than multi-source domain adaptation. The efficacy of emotion classification is hampered by the restricted information and volume from a single source domain. Thus, to improve the performance of domain adaptation, we present a novel multi-source domain adaptation approach for emotion classification, by combining broad learning and deep learning in this article. Specifically, we first design a model to extract domain-invariant features from each source domain to the same target domain by using BERT and Bi-LSTM, which can better capture contextual features. Then we adopt broad learning to train multiple classifiers based on the domain-invariant features, which can more effectively conduct multi-label classification tasks. In addition, we design a co-training model to boost these classifiers. Finally, we carry out several experiments on four datasets by comparison with the baseline methods. The experimental results show that our proposed approach can significantly outperform the baseline methods for textual emotion classification.(c) 2022 Published by Elsevier B.V.
资助项目	National Natural Sci- ence Foundation of China ; Ministry of Education of Humanities and Social Science project ; [61876205] ; [20YJAZH118]
WOS研究方向	Computer Science
语种	英语
出版者	ELSEVIER
WOS记录号	WOS:000905783200001
资助机构	National Natural Sci- ence Foundation of China ; Ministry of Education of Humanities and Social Science project
内容类型	期刊论文
源URL	[http://ir.ia.ac.cn/handle/173211/51108]
专题	复杂系统认知与决策实验室
通讯作者	Cao, Lihong
作者单位	1.Soochow Univ, Sch Comp Sci & Technol, Suzhou, Peoples R China 2.Chinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing, Peoples R China 3.Beihang Univ, State Key Lab Virtual Real Technol & Syst, Beijing 100191, Peoples R China 4.Lingnan Normal Univ, Sch Comp Sci & Intelligence Educ, Zhanjiang 524048, Peoples R China 5.South China Normal Univ, Guangdong Prov Key Lab Nanophoton Funct Mat & Devi, Guangzhou 510006, Peoples R China 6.Guangdong Univ Foreign Studies, Lab Language Engn & Comp, Guangzhou 510006, Peoples R China
推荐引用方式 GB/T 7714	Peng, Sancheng,Zeng, Rong,Cao, Lihong,et al. Multi-source domain adaptation method for textual emotion classification using deep and broad learning[J]. KNOWLEDGE-BASED SYSTEMS,2023,260:9.
APA	Peng, Sancheng.,Zeng, Rong.,Cao, Lihong.,Yang, Aimin.,Niu, Jianwei.,...&Zhou, Guodong.(2023).Multi-source domain adaptation method for textual emotion classification using deep and broad learning.KNOWLEDGE-BASED SYSTEMS,260,9.
MLA	Peng, Sancheng,et al."Multi-source domain adaptation method for textual emotion classification using deep and broad learning".KNOWLEDGE-BASED SYSTEMS 260(2023):9.