{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,26]],"date-time":"2026-08-26T05:58:44Z","timestamp":1787723924069,"version":"build-2784847793"},"reference-count":28,"publisher":"SAGE Publications","issue":"6","license":[{"start":{"date-parts":[[2018,3,7]],"date-time":"2018-03-07T00:00:00Z","timestamp":1520380800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/journals.sagepub.com\/page\/policies\/text-and-data-mining-license"}],"content-domain":{"domain":["journals.sagepub.com"],"crossmark-restriction":true},"short-container-title":["Journal of Information Science"],"published-print":{"date-parts":[[2018,12]]},"abstract":"<jats:p>This study presents a new dataset to be used in emotion extraction studies in Turkish text. We consider emotion extraction as a supervised text classification problem, which thereby requires a dataset for the training process. To satisfy this requirement, we aim to create a new dataset containing data for the six emotion categories: happiness, fear, anger, sadness, disgust and surprise. To gather this dataset, we conducted a survey and collected 27,350 entries from 4709 individuals. In the next step, we performed a validation process in which annotators validated each entry one by one by assigning a related emotion category. As a result of this process, we obtained two datasets, one raw and the other validated. Subsequently, we generated four versions of these two datasets using two different stemming methods and then modelled them using a vector space model. Then, we ran machine learning algorithms, including complement naive Bayes (CNB), random forest (RF), decision tree C4.5 (J48) and an updated version of support vector machines (SVMs), on the models to calculate the accuracy, precision, recall and F-measure values. Based on the results we obtained, we concluded that the SVM classifier yielded the highest performance value and that the models trained with a validated dataset provide more accurate results than the models trained with a non-validated dataset.<\/jats:p>","DOI":"10.1177\/0165551518761014","type":"journal-article","created":{"date-parts":[[2018,3,7]],"date-time":"2018-03-07T08:22:32Z","timestamp":1520410952000},"page":"848-860","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":29,"title":["TREMO: A dataset for emotion analysis in Turkish"],"prefix":"10.1177","volume":"44","author":[{"given":"Mansur Alp","family":"Tocoglu","sequence":"first","affiliation":[{"name":"Department of Computer Engineering, Faculty of Engineering, Dokuz Eyl\u00fcl University, Turkey"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Adil","family":"Alpkocak","sequence":"additional","affiliation":[{"name":"Department of Computer Engineering, Faculty of Engineering, Dokuz Eyl\u00fcl University, Turkey"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"179","published-online":{"date-parts":[[2018,3,7]]},"reference":[{"key":"bibr1-0165551518761014","doi-asserted-by":"publisher","DOI":"10.1177\/0165551515620551"},{"key":"bibr2-0165551518761014","doi-asserted-by":"publisher","DOI":"10.1037\/0022-3514.66.2.310"},{"key":"bibr3-0165551518761014","doi-asserted-by":"publisher","DOI":"10.1111\/j.1467-8640.2012.00456.x"},{"key":"bibr4-0165551518761014","first-page":"53","volume-title":"Volume 2: Proceedings of the AISB 2008 symposium on affective language in human and machine","author":"Danisman T"},{"key":"bibr5-0165551518761014","first-page":"1077","volume-title":"Proceedings of the 31st annual ACM symposium on applied computing","author":"Giachanou A"},{"key":"bibr6-0165551518761014","doi-asserted-by":"publisher","DOI":"10.1007\/s11280-013-0268-7"},{"key":"bibr7-0165551518761014","unstructured":"Go A, Bhayani R, Huang L. Twitter sentiment classification using distant supervision. Project Report CS224N, 2009. 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Master\u2019s Thesis, Department of Computer Engineering, Middle East Technical University, Ankara, Turkey, 2014."},{"key":"bibr18-0165551518761014","doi-asserted-by":"publisher","DOI":"10.1080\/02699939208411068"},{"key":"bibr19-0165551518761014","doi-asserted-by":"publisher","DOI":"10.1177\/001316446002000104"},{"key":"bibr20-0165551518761014","doi-asserted-by":"publisher","DOI":"10.1002\/asi.20750"},{"key":"bibr21-0165551518761014","unstructured":"Akin AA, Akin MD. Zemberek: an open source NLP framework for Turkic languages. 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