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.accessioned2025-01-06T17:29:45Z
dc.date.available2025-01-06T17:29:45Z
dc.date.issued2017
dc.description25th Signal Processing and Communications Applications Conference, SIU 2017 -- 15 May 2017 through 18 May 2017 -- Antalya -- 128703
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. © 2017 IEEE.
dc.identifier.doi10.1109/SIU.2017.7960388
dc.identifier.isbn978-150906494-6
dc.identifier.scopus2-s2.0-85026305925
dc.identifier.urihttps://doi.org/10.1109/SIU.2017.7960388
dc.identifier.urihttps://hdl.handle.net/20.500.14669/1339
dc.indekslendigikaynakScopus
dc.language.isotr
dc.publisherInstitute of Electrical and Electronics Engineers Inc.
dc.relation.ispartof2017 25th Signal Processing and Communications Applications Conference, SIU 2017
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.snmzKA_20241211
dc.subjectfeature selection
dc.subjectsentiment analysis
dc.subjecttext classification
dc.titleComparison of feature selection methods for sentiment analysis on Turkish Twitter data
dc.title.alternativeTÜrkçe Twitter Verilerinde Duygu Analizi için Nitelik Seçim Yöntemlerinin Karşilaştirilmasi
dc.typeConference Object

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