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Pretraining and Fine-Tuning Strategies for Sentiment Analysis of Latvian Tweets
Gaurish Thakkar, Mārcis Pinnis
55 - 61
10.3233/FAIA200602
Research Article
Frontiers in Artificial Intelligence and Applications
Volume 328: Human Language Technologies – The Baltic Perspective
Abstract

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.

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