Emotional contagion-based social sentiment mining in social networks by introducing network communities

Wang, X; He, D; Jin, D; Musial, K; Liu, M; Dang, J

Emotional contagion-based social sentiment mining in social networks by introducing network communities

Wang, X He, D Jin, D Musial, K

Liu, M Dang, J

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Publication Type:: Conference Proceeding
Citation:: International Conference on Information and Knowledge Management, Proceedings, 2019, pp. 1763 - 1772
Issue Date:: 2019-11-03

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Field	Value	Language
dc.contributor.author	Wang, X	en_US
dc.contributor.author	He, D	en_US
dc.contributor.author	Jin, D	en_US
dc.contributor.author	Musial, K https://orcid.org/0000-0001-6038-7647	en_US
dc.contributor.author	Liu, M	en_US
dc.contributor.author	Dang, J	en_US
dc.date.issued	2019-11-03	en_US
dc.identifier.citation	International Conference on Information and Knowledge Management, Proceedings, 2019, pp. 1763 - 1772	en_US
dc.identifier.isbn	9781450369763	en_US
dc.identifier.uri	http://hdl.handle.net/10453/137710
dc.description.abstract	© 2019 Association for Computing Machinery. The rapid development of social media services has facilitated the communication of opinions through online news, blogs, microblogs, instant-messages, and so on. This article concentrates on the mining of readers' social sentiments evoked by social media materials. Existing methods are only applicable to a minority of social media like news portals with emotional voting information, while ignore the emotional contagion between writers and readers. However, incorporating such factors is challenging since the learned hidden variables would be very fuzzy (because of the short and noisy text in social networks). In this paper, we try to solve this problem by introducing a high-order network structure, i.e. communities. We first propose a new generative model called Community-Enhanced Social Sentiment Mining (CESSM), which 1) considers the emotional contagion between writers and readers to capture precise social sentiment, and 2) incorporates network communities to capture coherent topics. We then derive an inference algorithm based on Gibbs sampling. Empirical results show that, CESSM achieves significantly superior performance against the state-of-the-art techniques for text sentiment classification and interestingness in social sentiment mining.	en_US
dc.relation.ispartof	International Conference on Information and Knowledge Management, Proceedings	en_US
dc.relation.isbasedon	10.1145/3357384.3357941	en_US
dc.rights	info:eu-repo/semantics/restrictedAccess
dc.title	Emotional contagion-based social sentiment mining in social networks by introducing network communities	en_US
dc.type	Conference Proceeding
utslib.for	0801 Artificial Intelligence and Image Processing	en_US
utslib.for	0806 Information Systems	en_US
pubs.embargo.period	Not known	en_US
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
pubs.organisational-group	/University of Technology Sydney/Strength - AAI - Advanced Analytics Institute Research Centre
utslib.copyright.status	in_progress	*
pubs.publication-status	Published	en_US

Abstract:

© 2019 Association for Computing Machinery. The rapid development of social media services has facilitated the communication of opinions through online news, blogs, microblogs, instant-messages, and so on. This article concentrates on the mining of readers' social sentiments evoked by social media materials. Existing methods are only applicable to a minority of social media like news portals with emotional voting information, while ignore the emotional contagion between writers and readers. However, incorporating such factors is challenging since the learned hidden variables would be very fuzzy (because of the short and noisy text in social networks). In this paper, we try to solve this problem by introducing a high-order network structure, i.e. communities. We first propose a new generative model called Community-Enhanced Social Sentiment Mining (CESSM), which 1) considers the emotional contagion between writers and readers to capture precise social sentiment, and 2) incorporates network communities to capture coherent topics. We then derive an inference algorithm based on Gibbs sampling. Empirical results show that, CESSM achieves significantly superior performance against the state-of-the-art techniques for text sentiment classification and interestingness in social sentiment mining.

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http://hdl.handle.net/10453/137710