CNN-Based Broad Learning for Cross-Domain Emotion Classification

doi:10.26599/TST.2022.9010007

CORC > 自动化研究所 > 中国科学院自动化研究所 > 模式识别国家重点实验室 > 自然语言处理

	CNN-Based Broad Learning for Cross-Domain Emotion Classification
	Zeng, Rong 5; Liu, Hongzhan 5; Peng, Sancheng 4; Cao, Lihong 4; Yang, Aimin 3; Zong, Chengqing 2; Zhou, Guodong 1
刊名	TSINGHUA SCIENCE AND TECHNOLOGY
	2023-04-01
卷号	28 期号:2 页码:360-369
关键词	Measurement Deep learning Adaptation models Feature extraction Convolutional neural networks Data mining Task analysis cross-domain emotion classification CNN broad learning classifier co-training
ISSN号	1007-0214
DOI	10.26599/TST.2022.9010007
通讯作者	Liu, Hongzhan(lhzscnu@163.com) ; Peng, Sancheng(psc346@aliyun.com)
英文摘要	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.
WOS研究方向	Computer Science ; Engineering
语种	英语
出版者	TSINGHUA UNIV PRESS
WOS记录号	WOS:000862392800014
内容类型	期刊论文
源URL	[http://ir.ia.ac.cn/handle/173211/50400]
专题	模式识别国家重点实验室_自然语言处理
通讯作者	Liu, Hongzhan; Peng, Sancheng
作者单位	1.Soochow Univ, Sch Comp Sci & Technol, Suzhou 215031, Peoples R China 2.Chinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing 100190, Peoples R China 3.Lingnan Normal Univ, Sch Comp Sci & Intelligence Educ, Guangzhou 510006, Peoples R China 4.Guangdong Univ Foreign Studies, Lab Language Engn & Comp, Guangzhou 510006, Peoples R China 5.South China Normal Univ, Guangdong Prov Key Lab Nanophoton Funct Mat & Dev, Guangzhou 511400, Peoples R China
推荐引用方式 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.