Emotion Classification of Text Based on BERT and Broad Learning System

Peng, S; Zeng, R; Liu, H; Chen, G; Wu, R; Yang, A; Yu, S

Emotion Classification of Text Based on BERT and Broad Learning System

Peng, S Zeng, R Liu, H Chen, G Wu, R Yang, A Yu, S

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Publisher:: Springer International Publishing
Publication Type:: Conference Proceeding
Citation:: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2021, 12858 LNCS, pp. 382-396
Issue Date:: 2021-01-01

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Field	Value	Language
dc.contributor.author	Peng, S
dc.contributor.author	Zeng, R
dc.contributor.author	Liu, H
dc.contributor.author	Chen, G
dc.contributor.author	Wu, R
dc.contributor.author	Yang, A
dc.contributor.author	Yu, S https://orcid.org/0000-0003-4485-6743
dc.date.accessioned	2022-08-06T09:33:39Z
dc.date.available	2022-08-06T09:33:39Z
dc.date.issued	2021-01-01
dc.identifier.citation	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2021, 12858 LNCS, pp. 382-396
dc.identifier.isbn	9783030858957
dc.identifier.issn	0302-9743
dc.identifier.issn	1611-3349
dc.identifier.uri	http://hdl.handle.net/10453/159707
dc.description.abstract	Emotion classification is one of the most important tasks of natural language processing (NLP). It focuses on identifying each kind of emotion expressed in text. However, most of the existing models are based on deep learning methods, which often suffer from long training time, difficulties in convergence and theoretical analysis. To solve the above problems, we propose a method for emotion classification of text based on bidirectional encoder representation from transformers (BERT) and broad learning system (BLS) in this paper. The texts are input into BERT pre-trained model to obtain context-related word embeddings and all word vectors are averaged to obtain sentence embedding. The feature nodes and enhancement nodes of BLS are used to extract the linear and nonlinear features of text, and three cascading structures of BLS are designed to transform input data to improve the ability of text feature extraction. The two groups of features are fused and input into the output layer to obtain the probability distribution of each kind of emotion, so as to achieve emotion classification. Extensive experiments are conducted on datasets from SemEval-2019 Task 3 and SMP2020-EWECT, and the experimental results show that our proposed method can better reduce the training time and improve the classification performance than that of the baseline methods.
dc.language	en
dc.publisher	Springer International Publishing
dc.relation.ispartof	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
dc.relation.isbasedon	10.1007/978-3-030-85896-4_30
dc.rights	info:eu-repo/semantics/closedAccess
dc.subject.classification	Artificial Intelligence & Image Processing
dc.title	Emotion Classification of Text Based on BERT and Broad Learning System
dc.type	Conference Proceeding
utslib.citation.volume	12858 LNCS
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Computer Science
utslib.copyright.status	closed_access	*
dc.date.updated	2022-08-06T09:33:38Z
pubs.publication-status	Published
pubs.volume	12858 LNCS

Abstract:

Emotion classification is one of the most important tasks of natural language processing (NLP). It focuses on identifying each kind of emotion expressed in text. However, most of the existing models are based on deep learning methods, which often suffer from long training time, difficulties in convergence and theoretical analysis. To solve the above problems, we propose a method for emotion classification of text based on bidirectional encoder representation from transformers (BERT) and broad learning system (BLS) in this paper. The texts are input into BERT pre-trained model to obtain context-related word embeddings and all word vectors are averaged to obtain sentence embedding. The feature nodes and enhancement nodes of BLS are used to extract the linear and nonlinear features of text, and three cascading structures of BLS are designed to transform input data to improve the ability of text feature extraction. The two groups of features are fused and input into the output layer to obtain the probability distribution of each kind of emotion, so as to achieve emotion classification. Extensive experiments are conducted on datasets from SemEval-2019 Task 3 and SMP2020-EWECT, and the experimental results show that our proposed method can better reduce the training time and improve the classification performance than that of the baseline methods.

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http://hdl.handle.net/10453/159707