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CNN-Based Broad Learning for Cross-Domain Emotion Classification

文献类型：期刊论文

作者Zeng, Rong1; Liu, Hongzhan1; Peng, Sancheng4; Cao, Lihong4; Yang, Aimin3; Zong, Chengqing5; Zhou, Guodong2
刊名TSINGHUA SCIENCE AND TECHNOLOGY
出版日期2023-04-01
卷号28期号:2页码:360-369
ISSN号1007-0214
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
DOI10.26599/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
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.South China Normal Univ, Guangdong Prov Key Lab Nanophoton Funct Mat & Dev, Guangzhou 511400, Peoples R China
2.Soochow Univ, Sch Comp Sci & Technol, Suzhou 215031, 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.Chinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing 100190, 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.

入库方式： OAI收割

来源：自动化研究所

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