Build emotion lexicon from microblogs by combining effects of seed words and emoticons in a heterogeneous graph

Song, K; Feng, S; Gao, W; Wang, D; Chen, L; Zhang, C

Build emotion lexicon from microblogs by combining effects of seed words and emoticons in a heterogeneous graph

Song, K Feng, S Gao, W Wang, D Chen, L

Zhang, C

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Publication Type:: Chapter
Citation:: Social Media Content Analysis: Natural Language Processing and Beyond, 2017, pp. 171 - 195
Issue Date:: 2017-01-01

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Field	Value	Language
dc.contributor.author	Song, K	en_US
dc.contributor.author	Feng, S	en_US
dc.contributor.author	Gao, W	en_US
dc.contributor.author	Wang, D	en_US
dc.contributor.author	Chen, L https://orcid.org/0000-0002-6468-5729	en_US
dc.contributor.author	Zhang, C https://orcid.org/0000-0001-5715-7154	en_US
dc.date.issued	2017-01-01	en_US
dc.identifier.citation	Social Media Content Analysis: Natural Language Processing and Beyond, 2017, pp. 171 - 195	en_US
dc.identifier.isbn	9789813223608	en_US
dc.identifier.uri	http://hdl.handle.net/10453/133504
dc.description.abstract	© 2018 by World Scientific Publishing Co. Pte. Ltd. All rights reserved. As an indispensable resource for emotion analysis, emotion lexicons have attracted increasing attention in recent years. Most existing methods focus on capturing the single emotional effect of words rather than the emotion distributions which are helpful to model multiple complex emotions in a subjective text. Meanwhile, automatic lexicon building methods are overly dependent on seed words but neglect the effect of emoticons which are natural graphical labels of fine-grained emotion. In this chapter, we describe a novel emotion lexicon building framework that leverages both seed words and emoticons simultaneously to capture emotion distributions of candidate words more accurately. Our method overcomes the weakness of existing methods by combining the effects of both seed words and emoticons in a unified three-layer heterogeneous graph, in which a multi-label random walk (MLRW) algorithm is performed to strengthen the emotion distribution estimation. Experimental results on real-world data reveal that our constructed emotion lexicon achieves promising results for emotion classification compared to the state-of-the-art lexicons.	en_US
dc.relation.ispartof	Social Media Content Analysis: Natural Language Processing and Beyond	en_US
dc.relation.isbasedon	10.1142/9789813223615_0013	en_US
dc.title	Build emotion lexicon from microblogs by combining effects of seed words and emoticons in a heterogeneous graph	en_US
dc.type	Chapter
utslib.for	0801 Artificial Intelligence and Image Processing	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/DVC (International)
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
pubs.organisational-group	/University of Technology Sydney/Strength - ACRI - Australia China Relations Institute
pubs.organisational-group	/University of Technology Sydney/Strength - CAI - Centre for Artificial Intelligence
utslib.copyright.status	closed_access
pubs.publication-status	Published	en_US

Abstract:

© 2018 by World Scientific Publishing Co. Pte. Ltd. All rights reserved. As an indispensable resource for emotion analysis, emotion lexicons have attracted increasing attention in recent years. Most existing methods focus on capturing the single emotional effect of words rather than the emotion distributions which are helpful to model multiple complex emotions in a subjective text. Meanwhile, automatic lexicon building methods are overly dependent on seed words but neglect the effect of emoticons which are natural graphical labels of fine-grained emotion. In this chapter, we describe a novel emotion lexicon building framework that leverages both seed words and emoticons simultaneously to capture emotion distributions of candidate words more accurately. Our method overcomes the weakness of existing methods by combining the effects of both seed words and emoticons in a unified three-layer heterogeneous graph, in which a multi-label random walk (MLRW) algorithm is performed to strengthen the emotion distribution estimation. Experimental results on real-world data reveal that our constructed emotion lexicon achieves promising results for emotion classification compared to the state-of-the-art lexicons.

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http://hdl.handle.net/10453/133504