A survey on deep learning for textual emotion analysis in social networks

Peng, S; Cao, L; Zhou, Y; Ouyang, Z; Yang, A; Li, X; Jia, W; Yu, S

A survey on deep learning for textual emotion analysis in social networks

Peng, S Cao, L Zhou, Y Ouyang, Z Yang, A Li, X Jia, W Yu, S

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Publisher:: Elsevier
Publication Type:: Journal Article
Citation:: Digital Communications and Networks, 2022, 8, (5), pp. 745-762
Issue Date:: 2022-01-01

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Field	Value	Language
dc.contributor.author	Peng, S
dc.contributor.author	Cao, L
dc.contributor.author	Zhou, Y
dc.contributor.author	Ouyang, Z
dc.contributor.author	Yang, A
dc.contributor.author	Li, X
dc.contributor.author	Jia, W
dc.contributor.author	Yu, S https://orcid.org/0000-0003-4485-6743
dc.date.accessioned	2023-04-21T01:17:57Z
dc.date.available	2023-04-21T01:17:57Z
dc.date.issued	2022-01-01
dc.identifier.citation	Digital Communications and Networks, 2022, 8, (5), pp. 745-762
dc.identifier.issn	2352-8648
dc.identifier.issn	2352-8648
dc.identifier.uri	http://hdl.handle.net/10453/170053
dc.description.abstract	Textual Emotion Analysis (TEA) aims to extract and analyze user emotional states in texts. Various Deep Learning (DL) methods have developed rapidly, and they have proven to be successful in many fields such as audio, image, and natural language processing. This trend has drawn increasing researchers away from traditional machine learning to DL for their scientific research. In this paper, we provide an overview on TEA based on DL methods. After introducing a background for emotion analysis that includes defining emotion, emotion classification methods, and application domains of emotion analysis, we summarize DL technology, and the word/sentence representation learning method. We then categorize existing TEA methods based on text structures and linguistic types: text-oriented monolingual methods, text conversations-oriented monolingual methods, text-oriented cross-linguistic methods, and emoji-oriented cross-linguistic methods. We close by discussing emotion analysis challenges and future research trends. We hope that our survey will assist interested readers in understanding the relationship between TEA and DL methods while also improving TEA development.
dc.language	English
dc.publisher	Elsevier
dc.relation.ispartof	Digital Communications and Networks
dc.relation.isbasedon	10.1016/j.dcan.2021.10.003
dc.rights	info:eu-repo/semantics/openAccess
dc.subject	0805 Distributed Computing, 1005 Communications Technologies, 1203 Design Practice and Management
dc.title	A survey on deep learning for textual emotion analysis in social networks
dc.type	Journal Article
utslib.citation.volume	8
utslib.for	0805 Distributed Computing
utslib.for	1005 Communications Technologies
utslib.for	1203 Design Practice and Management
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	open_access	*
pubs.consider-herdc	false
dc.date.updated	2023-04-21T01:17:54Z
pubs.issue	5
pubs.publication-status	Published
pubs.volume	8
utslib.citation.issue	5

Abstract:

Textual Emotion Analysis (TEA) aims to extract and analyze user emotional states in texts. Various Deep Learning (DL) methods have developed rapidly, and they have proven to be successful in many fields such as audio, image, and natural language processing. This trend has drawn increasing researchers away from traditional machine learning to DL for their scientific research. In this paper, we provide an overview on TEA based on DL methods. After introducing a background for emotion analysis that includes defining emotion, emotion classification methods, and application domains of emotion analysis, we summarize DL technology, and the word/sentence representation learning method. We then categorize existing TEA methods based on text structures and linguistic types: text-oriented monolingual methods, text conversations-oriented monolingual methods, text-oriented cross-linguistic methods, and emoji-oriented cross-linguistic methods. We close by discussing emotion analysis challenges and future research trends. We hope that our survey will assist interested readers in understanding the relationship between TEA and DL methods while also improving TEA development.

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http://hdl.handle.net/10453/170053