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

Version 2 2024-06-05, 11:45
Version 1 2014-10-28, 09:47
journal contribution
posted on 2024-06-05, 11:45 authored by Thin NguyenThin Nguyen, D Phung, B Adams, Svetha VenkateshSvetha Venkatesh
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.

History

Journal

Knowledge and information systems

Volume

37

Pagination

279-304

Location

London, England

ISSN

0219-1377

eISSN

0219-3116

Language

eng

Publication classification

C1 Refereed article in a scholarly journal

Copyright notice

2012, Springer-Verlag London

Issue

2

Publisher

Springer U K