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dc.contributor.authorNguyen, Thin
dc.contributor.authorPhung, Dinh
dc.contributor.authorAdams, Brett
dc.contributor.authorVenkatesh, Svetha
dc.contributor.editorCao, L., Huang, J.Z., Bailey, J., Koh, Y.S., Luo, J.
dc.date.accessioned2017-01-30T11:18:31Z
dc.date.available2017-01-30T11:18:31Z
dc.date.created2015-03-03T20:17:37Z
dc.date.issued2012
dc.identifier.citationNguyen, T. and Phung, D. and Adams, B. and Venkatesh, S. 2012. Emotional reactions to real-world events in social networks, in Cao, L. and Huang, J.Z. and Bailey, J. and Koh, Y.S. and Luo, J. (ed), 15th Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD), May 24-27 2011, pp. 53-64. Shenzhen, China: Springer-Verlag.
dc.identifier.urihttp://hdl.handle.net/20.500.11937/10396
dc.identifier.doi10.1007/978-3-642-28320-8_5
dc.description.abstract

A convergence of emotions among people in social networks is potentially resulted by the occurrence of an unprecedented event in real world. E.g., a majority of bloggers would react angrily at the September 11 terrorist attacks. Based on this observation, we introduce a sentiment index, computed from the current mood tags in a collection of blog posts utilizing an affective lexicon, potentially revealing subtle events discussed in the blogosphere. We then develop a method for extracting events based on this index and its distribution. Our second contribution is establishment of a new bursty structure in text streams termed a sentiment burst. We employ a stochastic model to detect bursty periods of moods and the events associated. Our results on a dataset of more than 12 million mood-tagged blog posts over a 4-year period have shown that our sentiment-based bursty events are indeed meaningful, in several ways.

dc.publisherSpringer-Verlag
dc.titleEmotional reactions to real-world events in social networks
dc.typeConference Paper
dcterms.source.volume7104
dcterms.source.startPage53
dcterms.source.endPage64
dcterms.source.issn0302-9743
dcterms.source.titleLecture Notes in Artificial Intelligence 7104. New Frontiers in Applied Data Mining
dcterms.source.seriesLecture Notes in Artificial Intelligence 7104. New Frontiers in Applied Data Mining
dcterms.source.conference15th Pacific-Asia Conference on Knowledge Discovery and Data Mining
dcterms.source.conference-start-dateMay 24 2011
dcterms.source.conferencelocationShenzhen, China
dcterms.source.placeGermany
curtin.departmentDepartment of Computing
curtin.accessStatusFulltext not available


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