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Analysis of data pre-processing methods for sentiment analysis of reviews

creativeworkseries.issn1508-2806
dc.contributor.authorParlar, Tuba
dc.contributor.authorÖzel, Selma Ayşe
dc.contributor.authorSong, Fei
dc.date.available2025-06-17T07:37:35Z
dc.date.issued2019
dc.descriptionBibliogr. s. 138-140.
dc.description.abstractThe goals of this study are to analyze the effects of data pre-processing methods for sentiment analysis and determine which of these pre-processing methods (and their combinations) are effective for English as well as for an agglutinative language like Turkish. We also try to answer the research question of whether there are any differences between agglutinative and non-agglutinative languages in terms of pre-processing methods for sentiment analysis. We find that the performance results for the English reviews are generally higher than those for the Turkish reviews due to the differences between the two languages in terms of vocabularies, writing styles, and agglutinative property of the Turkish language.en
dc.description.placeOfPublicationKraków
dc.description.versionwersja wydawnicza
dc.identifier.doihttps://doi.org/10.7494/csci.2019.20.1.3097
dc.identifier.eissn2300-7036
dc.identifier.issn1508-2806
dc.identifier.urihttps://repo.agh.edu.pl/handle/AGH/113224
dc.language.isoeng
dc.publisherWydawnictwa AGH
dc.relation.ispartofComputer Science
dc.rightsAttribution 4.0 International
dc.rights.accessotwarty dostęp
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/legalcode
dc.subjectdata pre-processingen
dc.subjectfeature selectionen
dc.subjectsentiment analysisen
dc.subjecttext classificationen
dc.titleAnalysis of data pre-processing methods for sentiment analysis of reviewsen
dc.title.relatedComputer Scienceen
dc.typeartykuł
dspace.entity.typePublication
publicationissue.issueNumberNo. 1
publicationissue.paginationpp. 123-141
publicationvolume.volumeNumberVol. 20
relation.isJournalIssueOfPublication0a53592a-d344-44ab-a173-c9ab7912b51d
relation.isJournalIssueOfPublication.latestForDiscovery0a53592a-d344-44ab-a173-c9ab7912b51d
relation.isJournalOfPublication020291ee-249b-4dcf-98a3-276a2f7981aa

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