Comparing pre-trained and fine-tuned transformer-based models for sentiment analysis in Turkish comments in student surveys

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dc.authorid0009-0000-5832-3062
dc.authorid0000-0001-7390-771X
dc.authorid0000-0002-7975-8628
dc.contributor.authorPourjalil, Kajalen_US
dc.contributor.authorEkin, Emineen_US
dc.contributor.authorRecal, Füsunen_US
dc.date.accessioned2025-09-26T11:25:19Z
dc.date.available2025-09-26T11:25:19Z
dc.date.issued2025-08-15
dc.departmentIşık Üniversitesi, Lisansüstü Eğitim Enstitüsü, Bilgisayar Mühendisliği Yüksek Lisans Programıen_US
dc.departmentIşık University, School of Graduate Studies, Master’s Program in Computer Engineeringen_US
dc.departmentIşık Üniversitesi, Mühendislik ve Doğa Bilimleri Fakültesi, Bilgisayar Mühendisliği Bölümüen_US
dc.departmentIşık University, Faculty of Engineering and Natural Sciences, Department of Computer Engineeringen_US
dc.departmentIşık Üniversitesi, Mühendislik ve Doğa Bilimleri Fakültesi, Endüstri Mühendisliği Bölümüen_US
dc.departmentIşık University, Faculty of Engineering and Natural Sciences, Department of Industrial Engineeringen_US
dc.description.abstractStudent surveys are essential for evaluating teaching quality and course content, but analyzing open-ended responses is challenging due to their unstructured and multilingual nature. This study applies sentiment analysis to Turkish educational survey responses using three transformer-based models: SAVASY, DBMDZ BERT Base Turkish Cased, and XLM-RoBERTa Base. A labeled dataset of real-world student comments was used, with sentiment labels assigned using the Gemini AI tool to facilitate model fine-tuning. Evaluation metrics included accuracy, F1-score, precision, recall, and confidence scores. Results show that fine-tuning improves sentiment classification, effectively identifying positive, negative, and neutral sentiments. This highlights the value of transformer models in analyzing Turkish student feedback.en_US
dc.description.versionPublisher's Versionen_US
dc.identifier.doi10.1109/SIU66497.2025.11112237
dc.identifier.endpage4
dc.identifier.isbn9798331566555
dc.identifier.isbn9798331566562
dc.identifier.issn2165-0608
dc.identifier.scopus2-s2.0-105015474601
dc.identifier.scopusqualityN/A
dc.identifier.startpage1
dc.identifier.urihttps://hdl.handle.net/11729/6726
dc.identifier.urihttps://doi.org/10.1109/SIU66497.2025.11112237
dc.identifier.wosWOS:001575462500260
dc.identifier.wosqualityN/A
dc.indekslendigikaynakScopusen_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakConference Proceedings Citation Index – Science (CPCI-S)en_US
dc.institutionauthorPourjalil, Kajalen_US
dc.institutionauthorEkin, Emineen_US
dc.institutionauthorRecal, Füsunen_US
dc.institutionauthorid0009-0000-5832-3062
dc.institutionauthorid0000-0001-7390-771X
dc.institutionauthorid0000-0002-7975-8628
dc.language.isoenen_US
dc.peerreviewedYesen_US
dc.publicationstatusPublisheden_US
dc.publisherInstitute of Electrical and Electronics Engineers Inc.en_US
dc.relation.ispartof2025 33rd Signal Processing and Communications Applications Conference (SIU)en_US
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Öğrencien_US
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectSentiment analysisen_US
dc.subjectTransformer modelsen_US
dc.subjectFineTuningen_US
dc.subjectPre-trainingen_US
dc.subjectStudent surveysen_US
dc.subjectCurriculaen_US
dc.subjectEducation computingen_US
dc.subjectFeedbacken_US
dc.subjectInformation systemsen_US
dc.subjectIntegrated circuitsen_US
dc.subjectLabeled dataen_US
dc.subjectStudentsen_US
dc.subjectTeachingen_US
dc.subjectCourse contentsen_US
dc.subjectFine tuningen_US
dc.subjectOpen-ended responseen_US
dc.subjectQuality contenten_US
dc.subjectSentiment analysisen_US
dc.subjectTeaching qualityen_US
dc.subjectTransformer modelingen_US
dc.subjectTurkishsen_US
dc.titleComparing pre-trained and fine-tuned transformer-based models for sentiment analysis in Turkish comments in student surveysen_US
dc.title.alternativeÖn eğitimli ve ince ayarlı transformator tabanlı modellerin öğrenci anketlerindeki Türkçe yorumlar için duygu analizi işlevinde karşılaştırılmasıen_US
dc.typeConference Objecten_US
dspace.entity.typePublicationen_US

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