CNN-Based Broad Learning for Cross-Domain Emotion Classification

doi:10.26599/TST.2022.9010007

	CNN-Based Broad Learning for Cross-Domain Emotion Classification
	Zeng, Rong 1; Liu, Hongzhan 1; Peng, Sancheng 2; Cao, Lihong 2; Yang, Aimin 3; Zong, Chengqing4 ; Zhou, Guodong 5
Source Publication	TSINGHUA SCIENCE AND TECHNOLOGY
ISSN	1007-0214
	2023-04-01
Volume	28 Issue:2 Pages:360-369
Corresponding Author	Liu, Hongzhan(lhzscnu@163.com) ; Peng, Sancheng(psc346@aliyun.com)
Abstract	Cross-domain emotion classification aims to leverage useful information in a source domain to help predict emotion polarity in a target domain in a unsupervised or semi-supervised manner. Due to the domain discrepancy, an emotion classifier trained on source domain may not work well on target domain. Many researchers have focused on traditional cross-domain sentiment classification, which is coarse-grained emotion classification. However, the problem of emotion classification for cross-domain is rarely involved. In this paper, we propose a method, called convolutional neural network (CNN) based broad learning, for cross-domain emotion classification by combining the strength of CNN and broad learning. We first utilized CNN to extract domain-invariant and domain-specific features simultaneously, so as to train two more efficient classifiers by employing broad learning. Then, to take advantage of these two classifiers, we designed a co-training model to boost together for them. Finally, we conducted comparative experiments on four datasets for verifying the effectiveness of our proposed method. The experimental results show that the proposed method can improve the performance of emotion classification more effectively than those baseline methods.
Keyword	Measurement Deep learning Adaptation models Feature extraction Convolutional neural networks Data mining Task analysis cross-domain emotion classification CNN broad learning classifier co-training
DOI	10.26599/TST.2022.9010007
Indexed By	SCI
Language	英语
WOS Research Area	Computer Science ; Engineering
WOS Subject	Computer Science, Information Systems ; Computer Science, Software Engineering ; Engineering, Electrical & Electronic
WOS ID	WOS:000862392800014
Publisher	TSINGHUA UNIV PRESS
Citation statistics
Document Type	期刊论文
Identifier	http://ir.ia.ac.cn/handle/173211/50401
Collection	模式识别国家重点实验室_自然语言处理
Corresponding Author	Liu, Hongzhan; Peng, Sancheng
Affiliation	1.South China Normal Univ, Guangdong Prov Key Lab Nanophoton Funct Mat & Dev, Guangzhou 511400, Peoples R China 2.Guangdong Univ Foreign Studies, Lab Language Engn & Comp, Guangzhou 510006, Peoples R China 3.Lingnan Normal Univ, Sch Comp Sci & Intelligence Educ, Guangzhou 510006, Peoples R China 4.Chinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing 100190, Peoples R China 5.Soochow Univ, Sch Comp Sci & Technol, Suzhou 215031, Peoples R China
Recommended Citation GB/T 7714	Zeng, Rong,Liu, Hongzhan,Peng, Sancheng,et al. CNN-Based Broad Learning for Cross-Domain Emotion Classification[J]. TSINGHUA SCIENCE AND TECHNOLOGY,2023,28(2):360-369.
APA	Zeng, Rong.,Liu, Hongzhan.,Peng, Sancheng.,Cao, Lihong.,Yang, Aimin.,...&Zhou, Guodong.(2023).CNN-Based Broad Learning for Cross-Domain Emotion Classification.TSINGHUA SCIENCE AND TECHNOLOGY,28(2),360-369.
MLA	Zeng, Rong,et al."CNN-Based Broad Learning for Cross-Domain Emotion Classification".TSINGHUA SCIENCE AND TECHNOLOGY 28.2(2023):360-369.

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