Review spam detection based on psychological and linguistic features; [Psikolojik ve Dilbilimsel Özniteliklere Dayali Istenmeyen Inceleme Metni Belirleme]

Simple item page
dc.contributor.authorOnan A.
dc.date.accessioned2024-07-22T08:09:38Z
dc.date.available2024-07-22T08:09:38Z
dc.date.issued2018
dc.description.abstractWith the advances in information and communication technologies, the immense quantity of review texts have become available on the Web. Review text can serve as an essential source of information for individual decision makers and business organizations. Some of the reviews shared on the Web may contain deceptive information to mislead the existing decision making process. In this study, we have presented a supervised learning based scheme for review spam detection. In the presented study, psychological and linguistic feature sets and their combinations are taken into consideration. In the study, the predictive performances of four conventional supervised learning methods (namely, Naive Bayes classifier, K-nearest neighbor algorithm, support vector machines and C4.5 algorithm) are evaluated on the different feature sets. © 2018 IEEE.
dc.identifier.DOI-ID10.1109/SIU.2018.8404388
dc.identifier.urihttp://akademikarsiv.cbu.edu.tr:4000/handle/123456789/14896
dc.language.isoTurkish
dc.publisherInstitute of Electrical and Electronics Engineers Inc.
dc.subjectClassifiers
dc.subjectDecision making
dc.subjectLearning algorithms
dc.subjectLearning systems
dc.subjectNearest neighbor search
dc.subjectPattern recognition
dc.subjectSignal processing
dc.subjectSupervised learning
dc.subjectDecision making process
dc.subjectInformation and Communication Technologies
dc.subjectK nearest neighbor algorithm
dc.subjectLearning based schemes
dc.subjectLinguistic features
dc.subjectNaive Bayes classifiers
dc.subjectPsychological features
dc.subjectSupervised learning methods
dc.subjectLinguistics
dc.titleReview spam detection based on psychological and linguistic features; [Psikolojik ve Dilbilimsel Özniteliklere Dayali Istenmeyen Inceleme Metni Belirleme]
dc.typeConference paper

Files

Collections

Scopus Koleksiyonu