Event extraction using behaviors of sentiment signals and burst structure in social media

Nguyen, Thin, Phung, Quoc-Dinh, Adams, B and Venkatesh, Svetha 2013, Event extraction using behaviors of sentiment signals and burst structure in social media, Knowledge and information systems, vol. 37, no. 2, pp. 279-304, doi: 10.1007/s10115-012-0494-9.

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Title Event extraction using behaviors of sentiment signals and burst structure in social media
Author(s) Nguyen, ThinORCID iD for Nguyen, Thin orcid.org/0000-0003-3467-8963
Phung, Quoc-DinhORCID iD for Phung, Quoc-Dinh orcid.org/0000-0002-9977-8247
Adams, B
Venkatesh, SvethaORCID iD for Venkatesh, Svetha orcid.org/0000-0001-8675-6631
Journal name Knowledge and information systems
Volume number 37
Issue number 2
Start page 279
End page 304
Total pages 26
Publisher Springer U K
Place of publication London, England
Publication date 2013-11
ISSN 0219-1377
Keyword(s) emotional reaction
sentiment index
sentiment burst
bursty event
Science & Technology
Computer Science, Artificial Intelligence
Computer Science, Information Systems
Computer Science
Summary Significant world events often cause the behavioral convergence of the expression of shared sentiment. This paper examines the use of the blogosphere as a framework to study user psychological behaviors, using their sentiment responses as a form of ‘sensor’ to infer real-world events of importance automatically. We formulate a novel temporal sentiment index function using quantitative measure of the valence value of bearing words in blog posts in which the set of affective bearing words is inspired from psychological research in emotion structure. The annual local minimum and maximum of the proposed sentiment signal function are utilized to extract significant events of the year and corresponding blog posts are further analyzed using topic modeling tools to understand their content. The paper then examines the correlation of topics discovered in relation to world news events reported by the mainstream news service provider, Cable News Network, and by using the Google search engine. Next, aiming at understanding sentiment at a finer granularity over time, we propose a stochastic burst detection model, extended from the work of Kleinberg, to work incrementally with stream data. The proposed model is then used to extract sentimental bursts occurring within a specific mood label (for example, a burst of observing ‘shocked’). The blog posts at those time indices are analyzed to extract topics, and these are compared to real-world news events. Our comprehensive set of experiments conducted on a large-scale set of 12 million posts from Livejournal shows that the proposed sentiment index function coincides well with significant world events while bursts in sentiment allow us to locate finer-grain external world events.
Language eng
DOI 10.1007/s10115-012-0494-9
Field of Research 080109 Pattern Recognition and Data Mining
080611 Information Systems Theory
0801 Artificial Intelligence And Image Processing
Socio Economic Objective 899999 Information and Communication Services not elsewhere classified
HERDC Research category C1 Refereed article in a scholarly journal
Copyright notice ©2012, Springer-Verlag London
Persistent URL http://hdl.handle.net/10536/DRO/DU:30048136

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