A novel wrapper feature selection algorithm based on iterated greedy metaheuristic for sentiment classification

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dc.contributor.authorGokalp, Osman
dc.contributor.authorTasci, Erdal
dc.contributor.authorUgur, Aybars
dc.date.accessioned2020-12-01T12:01:18Z
dc.date.available2020-12-01T12:01:18Z
dc.date.issued2020
dc.departmentEge Üniversitesien_US
dc.description.abstractIn recent years, sentiment analysis is becoming more and more important as the number of digital text resources increases in parallel with the development of information technology. Feature selection is a crucial sub-stage for the sentiment analysis as it can improve the overall predictive performance of a classifier while reducing the dimensionality of a problem. in this study, we propose a novel wrapper feature selection algorithm based on Iterated Greedy (IG) metaheuristic for sentiment classification. We also develop a selection procedure that is based on pre-calculated filter scores for the greedy construction part of the IG algorithm. A comprehensive experimental study is conducted on commonly-used sentiment analysis datasets to assess the performance of the proposed method. the computational results show that the proposed algorithm achieves 96.45% and 90.74% accuracy rates on average by using Multi-nomial Naive Bayes classifier for 9 public sentiment and 4 Amazon product reviews datasets, respectively. the results also reveal that our algorithm outperforms state-of-the-art results for the 9 public sentiment datasets. Moreover, the proposed algorithm produces highly competitive results with state-of-the-art feature selection algorithms for 4 Amazon datasets. (C) 2020 Elsevier Ltd. All rights reserved.en_US
dc.identifier.doi10.1016/j.eswa.2020.113176
dc.identifier.issn0957-4174
dc.identifier.issn1873-6793
dc.identifier.issn0957-4174en_US
dc.identifier.issn1873-6793en_US
dc.identifier.scopus2-s2.0-85077510592en_US
dc.identifier.scopusqualityN/Aen_US
dc.identifier.urihttps://doi.org/10.1016/j.eswa.2020.113176
dc.identifier.urihttps://hdl.handle.net/11454/62380
dc.identifier.volume146en_US
dc.identifier.wosWOS:000519653400017en_US
dc.identifier.wosqualityQ1en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherPergamon-Elsevier Science Ltden_US
dc.relation.ispartofExpert Systems With Applicationsen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectSentiment classificationen_US
dc.subjectFeature selectionen_US
dc.subjectIterated greedyen_US
dc.subjectMetaheuristicen_US
dc.subjectMachine learningen_US
dc.titleA novel wrapper feature selection algorithm based on iterated greedy metaheuristic for sentiment classificationen_US
dc.typeArticleen_US

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