A cross-validation study of Turkish sentiment analysis datasets and tools.
In recent years, sentiment analysis has gained increasing significance, prompting researchers to explore datasets in various languages, including Turkish. However, the limited availability and reuse of Turkish datasets across studies has yielded highly diverse outcomes. To address this, we conducted...
| Publicado en: | Language Resources & Evaluation Vol. 59; no. 4; pp. 4003 - 4042 |
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| Autores principales: | , , , |
| Formato: | Artículo |
| Publicado: |
Springer Nature
Dec2025
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| Materias: | |
| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=hlh&AN=189912041&site=ehost-live header: @attributes: shortDbName: hlh uiTerm: 189912041 longDbName: Humanities International Complete uiTag: AN controlInfo: bkinfo: jinfo: jid: 1574020X 179V jtl: Language Resources & Evaluation issn: 1574020X maglogo: N pubinfo: dt: Dec2025 vid: 59 iid: 4 pid: 237 pub: Springer Nature artinfo: ui: 189912041 10.1007/s10579-025-09869-6 ppf: 4003 ppct: 39 formats: fmt: – @attributes: type: T – @attributes: type: P size: 2.8MB tig: atl: A cross-validation study of Turkish sentiment analysis datasets and tools. aug: au: Çakıcı, Şevval Karaduman, Dilara Çırlan, Mehmet Akif Hürriyetoğlu, Ali affil: https://ror.org/01jjhfr75 Ozyegin University, İstanbul, Turkey https://ror.org/00jzwgz36 Koc University, Istanbul, Turkey https://ror.org/04qw24q55 Wageningen Food Safety Research, Wageningen, Netherlands su: Sentiment analysis Transformer models Language models Databases Model validation sug: subj: Sentiment analysis Transformer models Language models Databases Model validation keyword: Cross-validation Deep learning models Taxonomy Turkish dataset ab: In recent years, sentiment analysis has gained increasing significance, prompting researchers to explore datasets in various languages, including Turkish. However, the limited availability and reuse of Turkish datasets across studies has yielded highly diverse outcomes. To address this, we conducted a systematic review of sentiment analysis studies on Turkish text. Our search identified 78 relevant studies, from which we extracted over 80 datasets. These studies were labeled using a comprehensive sentiment analysis taxonomy, and the dataset details were compiled into a structured repository. Furthermore, we evaluated the performance of four state-of-the-art models-XLM-T, BERTurk (fine-tuned with the BounTi dataset), TSAM, and TurkishBERTweet-on four widely-used Turkish datasets. Among the models, XLM-T achieved the highest performance with an accuracy of 0.92 and F1 score of 0.95 on the Twt dataset, while TSAM reached 0.97 accuracy and F1 score on the Humir dataset. Our empirical results demonstrate that model performance varies significantly based on dataset characteristics such as domain, balance, and linguistic structure. Our review revealed key research gaps, including the limited application of emotion-based and concept-based sentiment analysis techniques and the lack of domain diversity in Turkish sentiment datasets. By highlighting such gaps and compiling a centralized repository, this study provides a comprehensive and publicly accessible resource to guide future research in Turkish sentiment analysis. pubtype: Academic Journal doctype: Article src: R language: English refInfo: copyright: @attributes: flag: Y custom: Language Resources & Evaluation is a copyright of Springer, 2025. All Rights Reserved. item: Language Resources & Evaluation holder: Springer Nature dt: @attributes: year: 2025 holdings: @attributes: islocal: N |
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