Topic enhanced sentiment spreading model in social networks considering user interest

Wang, X; Jin, D; Musial, K; Dang, J

Topic enhanced sentiment spreading model in social networks considering user interest

Wang, X Jin, D Musial, K Dang, J

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Publisher:: ASSOC ADVANCEMENT ARTIFICIAL INTELLIGENCE
Publication Type:: Conference Proceeding
Citation:: AAAI 2020 - 34th AAAI Conference on Artificial Intelligence, 2020, 34, pp. 989-996
Issue Date:: 2020-01-01

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Field	Value	Language
dc.contributor.author	Wang, X
dc.contributor.author	Jin, D
dc.contributor.author	Musial, K
dc.contributor.author	Dang, J
dc.date	2020-02-07
dc.date.accessioned	2023-03-26T23:53:53Z
dc.date.available	2023-03-26T23:53:53Z
dc.date.issued	2020-01-01
dc.identifier.citation	AAAI 2020 - 34th AAAI Conference on Artificial Intelligence, 2020, 34, pp. 989-996
dc.identifier.isbn	9781577358350
dc.identifier.issn	2159-5399
dc.identifier.issn	2374-3468
dc.identifier.uri	http://hdl.handle.net/10453/168486
dc.description.abstract	Emotion is a complex emotional state, which can affect our physiology and psychology and lead to behavior changes. The spreading process of emotions in the text-based social networks is referred to as sentiment spreading. In this paper, we study an interesting problem of sentiment spreading in social networks. In particular, by employing a text-based social network (Twitter), we try to unveil the correlation between users’ sentimental statuses and topic distributions embedded in the tweets, then to automatically learn the influence strength between linked users. Furthermore, we introduce user interest to refine the influence strength. We develop a unified probabilistic framework to formalize the problem into a topic-enhanced sentiment spreading model. The model can predict users’ sentimental statuses based on their historical emotional status, topic distributions in tweets and social structures. Experiments on the Twitter dataset show that the proposed model significantly outperforms several alternative methods in predicting users’ sentimental status. We also discover an intriguing phenomenon that positive and negative sentiment is more relevant to user interest than neutral ones. Our method offers a new opportunity to understand the underlying mechanism of sentimental spreading in online social networks.
dc.language	en
dc.publisher	ASSOC ADVANCEMENT ARTIFICIAL INTELLIGENCE
dc.relation.ispartof	AAAI 2020 - 34th AAAI Conference on Artificial Intelligence
dc.relation.ispartof	34th AAAI Conference on Artificial Intelligence / 32nd Innovative Applications of Artificial Intelligence Conference / 10th AAAI Symposium on Educational Advances in Artificial Intelligence
dc.relation.ispartofseries	AAAI Conference on Artificial Intelligence
dc.rights	info:eu-repo/semantics/closedAccess
dc.title	Topic enhanced sentiment spreading model in social networks considering user interest
dc.type	Conference Proceeding
utslib.citation.volume	34
utslib.location.activity	New York, NY
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/Strength - AAI - Advanced Analytics Institute Research Centre
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	2023-03-26T23:53:52Z
pubs.finish-date	2020-02-12
pubs.publication-status	Published
pubs.start-date	2020-02-07
pubs.volume	34

Abstract:

Emotion is a complex emotional state, which can affect our physiology and psychology and lead to behavior changes. The spreading process of emotions in the text-based social networks is referred to as sentiment spreading. In this paper, we study an interesting problem of sentiment spreading in social networks. In particular, by employing a text-based social network (Twitter), we try to unveil the correlation between users’ sentimental statuses and topic distributions embedded in the tweets, then to automatically learn the influence strength between linked users. Furthermore, we introduce user interest to refine the influence strength. We develop a unified probabilistic framework to formalize the problem into a topic-enhanced sentiment spreading model. The model can predict users’ sentimental statuses based on their historical emotional status, topic distributions in tweets and social structures. Experiments on the Twitter dataset show that the proposed model significantly outperforms several alternative methods in predicting users’ sentimental status. We also discover an intriguing phenomenon that positive and negative sentiment is more relevant to user interest than neutral ones. Our method offers a new opportunity to understand the underlying mechanism of sentimental spreading in online social networks.

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http://hdl.handle.net/10453/168486