Publication:
A Comparative Study of Text Classifier for Mobile Crowdsensing Applications

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dc.ConferencedateNOV 21-23, 2017
dc.ConferencelocationBandung, INDONESIA
dc.ConferencenameInternational Conference on Social Sciences and Education (ICSSE)
dc.contributor.authorRajoo, Sen_US
dc.contributor.authorMagalingam, Pen_US
dc.contributor.authorIdris, NBen_US
dc.contributor.authorSamy, GNen_US
dc.contributor.authorMaarop, Nen_US
dc.contributor.authorShanmugam, Ben_US
dc.contributor.authorPerumal, Sen_US
dc.date.accessioned2024-05-29T03:25:34Z
dc.date.available2024-05-29T03:25:34Z
dc.date.issued2018
dc.description.abstractMobile reporting applications are useful mainly for reporting real-time issues related to public infrastructure, environmental or social incidents through smart mobile devices. The credibility of the cases reported are often a great challenge because users may report false information and as a result this affects the response team in the aspect of time, energy and other resources. Researchers in the past have developed many report trust estimation algorithms that focuses on user's location, behavior and reputation. We aim to analyze the textual part of a report. Text analyses have been used for email spam filtering and sentiment analysis but have not been used for false report identification. Therefore, the purpose of this study is to compare different text classification algorithms and propose a suitable classifier for distinguishing the genuine and fake reports. The comparative analysis can be used by other researchers in the area of false report or fake message identification.
dc.identifier.doi10.1166/asl.2018.11788
dc.identifier.epage689
dc.identifier.isbn1936-7317
dc.identifier.issn1936-6612
dc.identifier.issue1
dc.identifier.scopusWOS:000432354700198
dc.identifier.spage686
dc.identifier.urihttps://oarep.usim.edu.my/handle/123456789/11985
dc.identifier.volume24
dc.languageEnglish
dc.language.isoen_US
dc.publisherAmer Scientific Publishersen_US
dc.relation.ispartofAdvanced Science Letters
dc.sourceWeb Of Science (ISI)
dc.subjectMobile Crowdsensingen_US
dc.subjectFalse Reporten_US
dc.subjectText Classificationen_US
dc.subjectClassifieren_US
dc.titleA Comparative Study of Text Classifier for Mobile Crowdsensing Applications
dc.typeProceedings Paperen_US
dspace.entity.typePublication

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