The Effect of BERT, ELECTRA and ALBERT Language Models on Sentiment Analysis for Turkish Product Reviews

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dc.authorscopusid57202999818
dc.contributor.authorGuven Z.A.
dc.date.accessioned2023-01-12T20:23:23Z
dc.date.available2023-01-12T20:23:23Z
dc.date.issued2021
dc.departmentN/A/Departmenten_US
dc.description6th International Conference on Computer Science and Engineering, UBMK 2021 -- 15 September 2021 through 17 September 2021 -- -- 176826en_US
dc.description.abstractNowadays, shopping is done more comfortably and without time constraints with the throwing of e-commerce platforms. These platforms allow consumers to examine reviews before purchasing products. Thus, consumers can decide whether to buy a product with positive or negative comments about the products. In this paper, Turkish sentiment analysis was carried out on the product comments at the Hepsiburada platform. For sentiment analysis, firstly, the success of Random Forest, Naive Bayes and Logistic Regression machine learning methods was measured. Then, the effect of BERT, ELECTRA and ALBERT language models on sentiment analysis was analyzed and the success of language models was compared with machine learning methods. While Naive Bayes achieved the highest accuracy with 89.95% among machine learning methods, ELECTRA was the most successful with 92.54% among language models. As a result of the study, it has been shown that the ELECTRA and ALBERT language models are more successful than machine learning methods. © 2021 IEEEen_US
dc.identifier.doi10.1109/UBMK52708.2021.9559007
dc.identifier.endpage632en_US
dc.identifier.isbn9781665429085
dc.identifier.scopus2-s2.0-85125864252en_US
dc.identifier.scopusqualityN/Aen_US
dc.identifier.startpage629en_US
dc.identifier.urihttps://doi.org/10.1109/UBMK52708.2021.9559007
dc.identifier.urihttps://hdl.handle.net/11454/79697
dc.indekslendigikaynakScopusen_US
dc.language.isotren_US
dc.publisherInstitute of Electrical and Electronics Engineers Inc.en_US
dc.relation.ispartofProceedings - 6th International Conference on Computer Science and Engineering, UBMK 2021en_US
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectE-commerceen_US
dc.subjectLanguage Modelen_US
dc.subjectMachine Learningen_US
dc.subjectProduct Reviewen_US
dc.subjectSentiment Analysisen_US
dc.subjectClassifiersen_US
dc.subjectComputational linguisticsen_US
dc.subjectDecision treesen_US
dc.subjectElectronic commerceen_US
dc.subjectLogistic regressionen_US
dc.subjectMachine learningen_US
dc.subjectRandom forestsen_US
dc.subjectCommerce platformsen_US
dc.subjectE- commercesen_US
dc.subjectLanguage modelen_US
dc.subjectMachine learning methodsen_US
dc.subjectMachine-learningen_US
dc.subjectNaive bayesen_US
dc.subjectProduct reviewsen_US
dc.subjectSentiment analysisen_US
dc.subjectTime constraintsen_US
dc.subjectTurkishsen_US
dc.subjectSentiment analysisen_US
dc.titleThe Effect of BERT, ELECTRA and ALBERT Language Models on Sentiment Analysis for Turkish Product Reviewsen_US
dc.title.alternativeTürkce Ürün Yorumlari için BERT, ELECTRA ve ALBERT Dil Modellerinin Duygu Analizine Etkisien_US
dc.typeConference Objecten_US

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