{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,7,6]],"date-time":"2024-07-06T21:19:22Z","timestamp":1720300762994},"reference-count":0,"publisher":"IOS Press","license":[{"start":{"date-parts":[[2020,9,15]],"date-time":"2020-09-15T00:00:00Z","timestamp":1600128000000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2020,9,15]]},"abstract":"<jats:p>In this paper, we present various pre-training strategies that aid in improving the accuracy of the sentiment classification task. At first, we pre-train language representation models using these strategies and then fine-tune them on the downstream task. Experimental results on a time-balanced tweet evaluation set show the improvement over the previous technique. We achieve 76% accuracy for sentiment analysis on Latvian tweets, which is a substantial improvement over previous work.<\/jats:p>","DOI":"10.3233\/faia200602","type":"book-chapter","created":{"date-parts":[[2020,9,22]],"date-time":"2020-09-22T11:54:32Z","timestamp":1600775672000},"source":"Crossref","is-referenced-by-count":3,"title":["Pretraining and Fine-Tuning Strategies for Sentiment Analysis of Latvian Tweets"],"prefix":"10.3233","author":[{"given":"Gaurish","family":"Thakkar","sequence":"first","affiliation":[{"name":"Faculty of Humanities and Social Sciences, University of Zagreb, Ul. Ivana Lu\u010di\u0107a 3, 10000, Zagreb, Croatia"}]},{"given":"M\u0101rcis","family":"Pinnis","sequence":"additional","affiliation":[{"name":"Tilde, Vien\u012bbas gatve 75A, Riga, Latvia, LV-1004"},{"name":"University of Latvia, Rai\u0146a bulv. 19-125, Riga, Latvia, LV-1586"}]}],"member":"7437","container-title":["Frontiers in Artificial Intelligence and Applications","Human Language Technologies \u2013 The Baltic Perspective"],"original-title":[],"link":[{"URL":"http:\/\/ebooks.iospress.nl\/pdf\/doi\/10.3233\/FAIA200602","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2020,9,22]],"date-time":"2020-09-22T11:54:33Z","timestamp":1600775673000},"score":1,"resource":{"primary":{"URL":"http:\/\/ebooks.iospress.nl\/doi\/10.3233\/FAIA200602"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,9,15]]},"references-count":0,"URL":"https:\/\/doi.org\/10.3233\/faia200602","relation":{},"ISSN":["0922-6389","1879-8314"],"issn-type":[{"value":"0922-6389","type":"print"},{"value":"1879-8314","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,9,15]]}}}