Comparison of Feature Selection Methods for Sentiment Analysis on Turkish Twitter Data

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dc.contributor.authorParlar, Tuba
dc.contributor.authorSarac, Esra
dc.contributor.authorOzel, Selma Ayse
dc.date.accessioned2024-09-18T20:56:55Z
dc.date.available2024-09-18T20:56:55Z
dc.date.issued2017
dc.departmentHatay Mustafa Kemal Üniversitesien_US
dc.description25th Signal Processing and Communications Applications Conference (SIU) -- MAY 15-18, 2017 -- Antalya, TURKEYen_US
dc.description.abstractThe Internet and social media provide a major source of information about people's opinions. Due to the rapidly growing number of online documents, it becomes both time-consuming and hard task to obtain and analyze the desired opinionated information. Sentiment analysis is the classification of sentiments expressed in documents. To improve classification perfromance feature selection methods which help to identify the most valuable features are generally applied. In this paper, we compare the performance of four feature selection methods namely Chi-square, Information Gain, Query Expansion Ranking, and Ant Colony Optimization using Maximum Entropi Modeling classification algorithm over Turkish Twitter dataset. Therefore, the effects of feature selection methods over the performance of sentiment analysis of Turkish Twitter data are evaluated. Experimental results show that Query Expansion Ranking and Ant Colony Optimization methods outperform other traditional feature selection methods for sentiment analysis.en_US
dc.description.sponsorshipTurk Telekom,Arcelik A S,Aselsan,ARGENIT,HAVELSAN,NETAS,Adresgezgini,IEEE Turkey Sect,AVCR Informat Technologies,Cisco,i2i Syst,Integrated Syst & Syst Design,ENOVAS,FiGES Engn,MS Spektral,Istanbul Teknik Univen_US
dc.identifier.isbn978-1-5090-6494-6
dc.identifier.issn2165-0608
dc.identifier.scopus2-s2.0-85026305925en_US
dc.identifier.scopusqualityN/Aen_US
dc.identifier.urihttps://hdl.handle.net/20.500.12483/12184
dc.identifier.wosWOS:000413813100251en_US
dc.identifier.wosqualityN/Aen_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isotren_US
dc.publisherIeeeen_US
dc.relation.ispartof2017 25th Signal Processing and Communications Applications Conference (Siu)en_US
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectsentiment analysisen_US
dc.subjectfeature selectionen_US
dc.subjecttext classificationen_US
dc.titleComparison of Feature Selection Methods for Sentiment Analysis on Turkish Twitter Dataen_US
dc.typeConference Objecten_US

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