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dc.contributor.authorYelmen, İlkayen_US
dc.contributor.authorZontul, Metinen_US
dc.contributor.authorKaynar, Oğuzen_US
dc.contributor.authorSönmez, Ferdien_US
dc.date.accessioned2019-10-29T17:48:45Z
dc.date.available2019-10-29T17:48:45Z
dc.date.issued2018
dc.identifier.issn1998-4464
dc.identifier.urihttps://hdl.handle.net/20.500.12294/1944
dc.description.abstractThe increase in the amount of content shared on social media makes it difficult to extract meaningful information from scientific studies. Accordingly, in recent years, researchers have been working extensively on sentiment analysis studies for the automatic evaluation of social media data. One of the focuses of these studies is sentiment analysis on tweets. The more tweets are available, the more features in terms of words exist. This leads to the curse of dimensionality and sparsity, resulting in a decrease in the success of the classification. In this study, Gini Index, Information Gain and Genetic Algorithm (GA) are used for feature selection and Support Vector Machines (SVMs), Artificial Neural Networks (ANN) and Centroid Based classification algorithms are used for the classification of Turkish tweets obtained from 3 different GSM operators. The feature selection methods are combined with the classification methods to investigate the effect on the success rate of analysis. Especially, when the SVMs are used with the GA as a hybrid, 96.8% success has been achieved for the classification of the tweets as positive or negative. © 2018, North Atlantic University Union. All rights reserved.en_US
dc.description.sponsorshipWe would like to express our special appreciation and thanks to Turkish Airlines for the financial support.en_US
dc.language.isoengen_US
dc.publisherNorth Atlantic University Unionen_US
dc.relation.ispartofInternational Journal of Circuits, Systems and Signal Processingen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectClassification Algorithmsen_US
dc.subjectFeature Extractionen_US
dc.subjectGenetic Algorithmsen_US
dc.subjectSentiment Analysisen_US
dc.subjectText Miningen_US
dc.titleA novel hybrid approach for sentiment classification of Turkish tweets for GSM operatorsen_US
dc.typearticleen_US
dc.departmentİstanbul Arel Üniversitesi, Mühendislik-Mimarlık Fakültesi, Bilgisayar Mühendisliği Bölümüen_US
dc.identifier.volume12en_US
dc.identifier.startpage637en_US
dc.identifier.endpage645en_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.department-tempYelmen, I., Department Computer Engineering, Kadir Has University, Istanbul, 34083, Turkey; Zontul, M., Department Software Engineering, Istanbul Aydin University, Istanbul, 34153, Turkey; Kaynar, O., Department of Management Information Systems, Cumhuriyet University, Sivas, 58140, Turkey; Sonmez, F., Department Computer Engineering, Arel University, Istanbul, 34537, Turkeyen_US


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