IWD Based Feature Selection Algorithm for Sentiment Analysis

Basit Öğe Kaydı
dc.authoridSarac, Esra/0000-0002-2503-0084
dc.contributor.authorParlar, Tuba
dc.contributor.authorSarac, Esra
dc.date.accessioned2024-09-18T20:20:04Z
dc.date.available2024-09-18T20:20:04Z
dc.date.issued2019
dc.departmentHatay Mustafa Kemal Üniversitesien_US
dc.description.abstractFeature selection methods aim to improve the classification performance by eliminating non-valuable features. In this paper, our aim is to apply a recent optimization technique namely the Intelligent Water Drops (IWD) algorithm to select best features for sentiment analysis. We investigate the classification performances of our proposed IWD based feature selection method by comparing one of the well-known feature selection method using Maximum Entropy classifier. Experimental results show that Intelligent Water Drops based feature selection method outperforms than ReliefF method for sentiment analysis.en_US
dc.description.sponsorshipMustafa Kemal University Academic Research Project Unit [15426]en_US
dc.description.sponsorshipThis research is supported by Mustafa Kemal University Academic Research Project Unit (No. 15426).en_US
dc.identifier.doi10.5755/j01.eie.25.1.22736
dc.identifier.endpage58en_US
dc.identifier.issn1392-1215
dc.identifier.issue1en_US
dc.identifier.scopus2-s2.0-85061600093en_US
dc.identifier.scopusqualityQ3en_US
dc.identifier.startpage54en_US
dc.identifier.urihttps://doi.org/10.5755/j01.eie.25.1.22736
dc.identifier.urihttps://hdl.handle.net/20.500.12483/10035
dc.identifier.volume25en_US
dc.identifier.wosWOS:000458506100009en_US
dc.identifier.wosqualityQ4en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherKaunas Univ Technologyen_US
dc.relation.ispartofElektronika Ir Elektrotechnikaen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectFeature selectionen_US
dc.subjectMachine learningen_US
dc.subjectNatural language processingen_US
dc.subjectText miningen_US
dc.subjectSentiment analysisen_US
dc.titleIWD Based Feature Selection Algorithm for Sentiment Analysisen_US
dc.typeArticleen_US

Dosyalar

Orijinal paket
Listeleniyor 1 - 1 / 1
Küçük Resim
İsim:
Tam Metin / Full Text
Boyut:
576.96 KB
Biçim:
Adobe Portable Document Format
İndir

Koleksiyon

WoS İndeksli Yayınlar Koleksiyonu
Scopus İndeksli Yayınlar Koleksiyonu