MoSa: A modeling and sentiment analysis system for mobile application big data

Zhang, Y; Ren, W; Zhu, T; Bi, W

MoSa: A modeling and sentiment analysis system for mobile application big data

Zhang, Y Ren, W Zhu, T

Bi, W

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Publication Type:: Conference Proceeding
Citation:: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2018, 11335 LNCS pp. 582 - 595
Issue Date:: 2018-01-01

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Field	Value	Language
dc.contributor.author	Zhang, Y	en_US
dc.contributor.author	Ren, W	en_US
dc.contributor.author	Zhu, T https://orcid.org/0000-0003-3411-7947	en_US
dc.contributor.author	Bi, W	en_US
dc.date.issued	2018-01-01	en_US
dc.identifier.citation	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2018, 11335 LNCS pp. 582 - 595	en_US
dc.identifier.isbn	9783030050535	en_US
dc.identifier.issn	0302-9743	en_US
dc.identifier.uri	http://hdl.handle.net/10453/133837
dc.description.abstract	© Springer Nature Switzerland AG 2018. A large amount of data about ending users are generated in the interaction over mobile applications, which becomes a valuable data source for sensing human behaviors and public sentiment trends on some topics. Existing works concentrate on traditional feedback data from web sites, which usually come from desktops instead of from mobile terminals. Few studies have been conducted on interactive data from mobile applications such as news aggregation and recommendation applications. In this paper, we propose a system that can model feedback behaviors of mobile users, and can analyze sentiment trends in mobile feedbacks. The testing data are authentic and are dumped from the most frequently used mobile application in China called Toutiao. We propose several analysis methods on sentiment of comments, and modeling algorithms on feedback behaviors. We build a system called MoSa and by using the system, we discover several implicit behavior models and hidden sentiment trends as follows: During news spreading stage, the number of comments grow linearly per month with slope of 3 in 3 months; The dynamics of replying comments are positively correlated with personal daily routines in 24 h; Replying comment behaviors are much more rare than clicking agreement behaviors in mobile applications; The standard deviation of sentiment values in comments are highly influenced by timing stages. Our system and modeling methods provide empirical results for guiding interaction design in mobile Internet, social networks, and blockchain-based crowdsourcing.	en_US
dc.relation.ispartof	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)	en_US
dc.relation.isbasedon	10.1007/978-3-030-05054-2_44	en_US
dc.subject.classification	Artificial Intelligence & Image Processing	en_US
dc.title	MoSa: A modeling and sentiment analysis system for mobile application big data	en_US
dc.type	Conference Proceeding
utslib.citation.volume	11335 LNCS	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
utslib.copyright.status	closed_access
pubs.publication-status	Published	en_US
pubs.volume	11335 LNCS	en_US

Abstract:

© Springer Nature Switzerland AG 2018. A large amount of data about ending users are generated in the interaction over mobile applications, which becomes a valuable data source for sensing human behaviors and public sentiment trends on some topics. Existing works concentrate on traditional feedback data from web sites, which usually come from desktops instead of from mobile terminals. Few studies have been conducted on interactive data from mobile applications such as news aggregation and recommendation applications. In this paper, we propose a system that can model feedback behaviors of mobile users, and can analyze sentiment trends in mobile feedbacks. The testing data are authentic and are dumped from the most frequently used mobile application in China called Toutiao. We propose several analysis methods on sentiment of comments, and modeling algorithms on feedback behaviors. We build a system called MoSa and by using the system, we discover several implicit behavior models and hidden sentiment trends as follows: During news spreading stage, the number of comments grow linearly per month with slope of 3 in 3 months; The dynamics of replying comments are positively correlated with personal daily routines in 24 h; Replying comment behaviors are much more rare than clicking agreement behaviors in mobile applications; The standard deviation of sentiment values in comments are highly influenced by timing stages. Our system and modeling methods provide empirical results for guiding interaction design in mobile Internet, social networks, and blockchain-based crowdsourcing.

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http://hdl.handle.net/10453/133837