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In addition, the success of modelling using deep learning (DL) approaches depends on the sample size. More samples are needed for Turkish due to the unique characteristics of the language. However, emotion classification data sets in Turkish are quite limited. In this study, the pretrained language model approach was used to create a stronger emotion classification model for Turkish. Well-known pretrained language models were fine-tuned for this purpose. The performances of these fine-tuned models for Turkish emotion classification were comprehensively compared with the performances of TML and DL methods in experimental studies. The proposed approach provides state-of-the-art performance for Turkish emotion classification.<\/jats:p>","DOI":"10.1177\/0165551520985507","type":"journal-article","created":{"date-parts":[[2021,1,14]],"date-time":"2021-01-14T21:14:59Z","timestamp":1610658899000},"page":"857-865","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":10,"title":["A study of Turkish emotion classification with pretrained language models"],"prefix":"10.1177","volume":"48","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-2493-4022","authenticated-orcid":false,"given":"Alaettin","family":"U\u00e7an","sequence":"first","affiliation":[{"name":"Hacettepe University, Ankara, Turkey"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1127-515X","authenticated-orcid":false,"given":"Murat","family":"D\u00f6rterler","sequence":"additional","affiliation":[{"name":"Gazi University, Ankara, Turkey"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ebru","family":"Ak\u00e7ap\u0131nar Sezer","sequence":"additional","affiliation":[{"name":"Hacettepe University, Ankara, Turkey"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"179","published-online":{"date-parts":[[2021,1,12]]},"reference":[{"key":"bibr1-0165551520985507","doi-asserted-by":"publisher","DOI":"10.1080\/02699939208411068"},{"key":"bibr2-0165551520985507","volume-title":"The emotions","author":"Plutchik R","year":"1991"},{"key":"bibr3-0165551520985507","doi-asserted-by":"publisher","DOI":"10.1038\/nature14539"},{"key":"bibr4-0165551520985507","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-031-02165-7"},{"key":"bibr5-0165551520985507","doi-asserted-by":"publisher","DOI":"10.1007\/978-981-10-5209-5"},{"key":"bibr6-0165551520985507","volume-title":"Proceedings of the ICML 2014 31st international conference on international conference on machine learning","volume":"32","author":"Le QV"},{"key":"bibr7-0165551520985507","first-page":"170303130","author":"Lin Z","journal-title":"arXiv Prepr"},{"key":"bibr8-0165551520985507","unstructured":"Mikolov T, Corrado G, Chen K et al. 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