QER: a new feature selection method for sentiment analysis

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dc.authoridOzel, Selma Ayse/0000-0001-9201-6349
dc.contributor.authorParlar, Tuba
dc.contributor.authorOzel, Selma Ayse
dc.contributor.authorSong, Fei
dc.date.accessioned2024-09-18T20:56:55Z
dc.date.available2024-09-18T20:56:55Z
dc.date.issued2018
dc.departmentHatay Mustafa Kemal Üniversitesien_US
dc.description.abstractSentiment analysis is about the classification of sentiments expressed in review documents. In order to improve the classification accuracy, feature selection methods are often used to rank features so that non-informative and noisy features with low ranks can be removed. In this study, we propose a new feature selection method, called query expansion ranking, which is based on query expansion term weighting methods from the field of information retrieval. We compare our proposed method with other widely used feature selection methods, including Chi square, information gain, document frequency difference, and optimal orthogonal centroid, using four classifiers: na < ve Bayes multinomial, support vector machines, maximum entropy modelling, and decision trees. We test them on movie and multiple kinds of product reviews for both Turkish and English languages so that we can show their performances for different domains, languages, and classifiers. We observe that our proposed method achieves consistently better performance than other feature selection methods, and query expansion ranking, Chi square, information gain, document frequency difference methods tend to produce better results for both the English and Turkish reviews when tested using na < ve Bayes multinomial classifier.en_US
dc.description.sponsorshipCukurova University Fund of Scientific Research Projects [FDK-2015-3833]; Mustafa Kemal University Fund of Scientific Research Projects [15426]en_US
dc.description.sponsorshipThis research is supported by Cukurova University Fund of Scientific Research Projects under Grant No. FDK-2015-3833, and Mustafa Kemal University Fund of Scientific Research Projects under Grant No. 15426.en_US
dc.identifier.doi10.1186/s13673-018-0135-8
dc.identifier.issn2192-1962
dc.identifier.scopus2-s2.0-85046624696en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.urihttps://doi.org/10.1186/s13673-018-0135-8
dc.identifier.urihttps://hdl.handle.net/20.500.12483/12182
dc.identifier.volume8en_US
dc.identifier.wosWOS:000431867100001en_US
dc.identifier.wosqualityQ2en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherKorea Information Processing Socen_US
dc.relation.ispartofHuman-Centric Computing and Information Sciencesen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectSentiment analysisen_US
dc.subjectFeature selectionen_US
dc.subjectMachine learningen_US
dc.subjectText classificationen_US
dc.titleQER: a new feature selection method for sentiment analysisen_US
dc.typeArticleen_US

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