Knowledge Commons of Institute of Automation，CAS
| Multi-source domain adaptation method for textual emotion classification using deep and broad learning | |
Peng, Sancheng1; Zeng, Rong2; Cao, Lihong1; Yang, Aimin3; Niu, Jianwei4; Zong, Chengqing5 ; Zhou, Guodong6
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| Source Publication | KNOWLEDGE-BASED SYSTEMS
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| ISSN | 0950-7051 |
| 2023-01-25 | |
| Volume | 260Pages:9 |
| Corresponding Author | Cao, Lihong(201610130@oamail.gdufs.edu.cn) |
| Abstract | 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. |
| Keyword | Multi-domain Emotion classification BERT Broad learning Bi-LSTM |
| DOI | 10.1016/j.knosys.2022.110173 |
| Indexed By | SCI |
| Language | 英语 |
| Funding Project | National Natural Sci- ence Foundation of China ; Ministry of Education of Humanities and Social Science project ; [61876205] ; [20YJAZH118] |
| Funding Organization | National Natural Sci- ence Foundation of China ; Ministry of Education of Humanities and Social Science project |
| WOS Research Area | Computer Science |
| WOS Subject | Computer Science, Artificial Intelligence |
| WOS ID | WOS:000905783200001 |
| Publisher | ELSEVIER |
| Citation statistics | |
| Document Type | 期刊论文 |
| Identifier | http://ir.ia.ac.cn/handle/173211/51109 |
| Collection | 复杂系统认知与决策实验室 |
| Corresponding Author | Cao, Lihong |
| Affiliation | 1.Guangdong Univ Foreign Studies, Lab Language Engn & Comp, Guangzhou 510006, Peoples R China 2.South China Normal Univ, Guangdong Prov Key Lab Nanophoton Funct Mat & Devi, Guangzhou 510006, Peoples R China 3.Lingnan Normal Univ, Sch Comp Sci & Intelligence Educ, Zhanjiang 524048, Peoples R China 4.Beihang Univ, State Key Lab Virtual Real Technol & Syst, Beijing 100191, Peoples R China 5.Chinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing, Peoples R China 6.Soochow Univ, Sch Comp Sci & Technol, Suzhou, Peoples R China |
| Recommended Citation 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. |
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