Bursty event discovery from online news outlets

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dc.contributor.advisorGhorbani, Ali
dc.contributor.authorKochak, Seyed
dc.date.accessioned2023-03-01T16:39:50Z
dc.date.available2023-03-01T16:39:50Z
dc.date.issued2015
dc.date.updated2016-11-21T00:00:00Z
dc.description.abstractOn this thesis, we have developed a set of methods along with a framework for discovery of bursty events and their relationship from streams of online news articles. Bursty event discovery can be done using the discovered bursty terms which are significantly smaller in size compared to the original feature-set. Moreover, the discovered bursty events are compared in order to discover any potential relational link between any of two. It is the assumption of this work that bursty events and their relationship in time can provide useful information to firms and individuals who their decision making process is significantly affected by news events. The system performed at 64% level of accuracy on a real world dataset. The results show a great promise as do the implicit measures that our proposed framework and methods can be utilized towards real world applications.
dc.description.copyrightNot available for use outside of the University of New Brunswick
dc.description.note(UNB thesis number) Thesis 9566. (OCoLC)963858008. Electronic Only.
dc.description.noteM.C.S. University of New Brunswick, Faculty Computer Science, 2015.
dc.formattext/xml
dc.format.extentxii, 78 pages : illustrations
dc.format.mediumelectronic
dc.identifier.oclc(OCoLC)963858008
dc.identifier.otherThesis 9566
dc.identifier.urihttps://unbscholar.lib.unb.ca/handle/1882/14291
dc.language.isoen_CA
dc.publisherUniversity of New Brunswick
dc.rightshttp://purl.org/coar/access_right/c_abf2
dc.subject.classificationText mining.
dc.subject.classificationBursty events.
dc.subject.classificationEvent detection.
dc.subject.disciplineComputer Science
dc.subject.lcshEvent processing (Computer science)
dc.subject.lcshInformation retrieval.
dc.subject.lcshCluster analysis ‡x Computer programs.
dc.subject.lcshNews articles.
dc.subject.lcshNews Web sites.
dc.subject.lcshElectronic newspapers.
dc.subject.lcshData mining.
dc.titleBursty event discovery from online news outlets
dc.typemaster thesis
thesis.degree.disciplineComputer Science
thesis.degree.fullnameMaster of Computer Science
thesis.degree.grantorUniversity of New Brunswick
thesis.degree.levelmasters
thesis.degree.nameM.C.S.

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