Ensemble methods for opinion mining; [Görüş Madenciliʇinde Siniflandirici Topluluklari]

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dc.contributor.authorOnan A.
dc.contributor.authorKorukoglu S.
dc.date.accessioned2024-07-22T08:13:24Z
dc.date.available2024-07-22T08:13:24Z
dc.date.issued2015
dc.description.abstractOpinion mining is an emerging field which uses computer science methods to extract subjective information, such as opinion, emotion, and attitude inherent in opinion holder's text. One of the major issues in opinion mining is to enhance the predictive performance of classification algorithm. Ensemble methods used for opinion mining aim to obtain robust classification models by combining decisions obtained by multiple classifier training, rather than depending on a single classifier. In this study, the comparative performance of opinion mining datasets on Bagging, Dagging, Random Subspace and Adaboost ensemble methods with five different classifiers and six different data representation schemes are presented. The experimental results indicate that ensemble methods can be used for building efficient opinion mining classification methods. © 2015 IEEE.
dc.identifier.DOI-ID10.1109/SIU.2015.7129796
dc.identifier.urihttp://akademikarsiv.cbu.edu.tr:4000/handle/123456789/16332
dc.language.isoTurkish
dc.publisherInstitute of Electrical and Electronics Engineers Inc.
dc.subjectAdaptive boosting
dc.subjectClassification (of information)
dc.subjectSignal processing
dc.subjectText mining
dc.subjectTrees (mathematics)
dc.subjectClassification algorithm
dc.subjectComparative performance
dc.subjectEnsemble methods
dc.subjectMultiple classifiers
dc.subjectOpinion mining
dc.subjectPredictive performance
dc.subjectRobust classification
dc.subjectSubjective information
dc.subjectSentiment analysis
dc.titleEnsemble methods for opinion mining; [Görüş Madenciliʇinde Siniflandirici Topluluklari]
dc.typeConference paper

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