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

dc.contributor.authorParlar, Tuba
dc.contributor.authorSarac, Esra
dc.contributor.authorOzel, Selma Ayse
dc.date.accessioned2025-01-06T17:44:20Z
dc.date.available2025-01-06T17:44:20Z
dc.date.issued2017
dc.description25th Signal Processing and Communications Applications Conference (SIU) -- MAY 15-18, 2017 -- Antalya, TURKEY
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.
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 Univ
dc.identifier.isbn978-1-5090-6494-6
dc.identifier.issn2165-0608
dc.identifier.urihttps://hdl.handle.net/20.500.14669/3006
dc.identifier.wosWOS:000413813100251
dc.identifier.wosqualityN/A
dc.indekslendigikaynakWeb of Science
dc.language.isotr
dc.publisherIEEE
dc.relation.ispartof2017 25th Signal Processing and Communications Applications Conference (Siu)
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.snmzKA_20241211
dc.subjectsentiment analysis
dc.subjectfeature selection
dc.subjecttext classification
dc.titleComparison of Feature Selection Methods for Sentiment Analysis on Turkish Twitter Data
dc.typeConference Object

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