@inproceedings{c286b969d5aa4f079d1d1818159efa61,
title = "Pretraining and fine-tuning strategies for sentiment analysis of latvian tweets",
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.",
keywords = "BERT, Latvian, Sentiment analysis, Word embeddings",
author = "Gaurish Thakkar and Mārcis Pinnis",
note = "Publisher Copyright: {\textcopyright} 2020 The authors and IOS Press.",
year = "2020",
month = sep,
day = "15",
doi = "10.3233/FAIA200602",
language = "English",
isbn = "978-164368116-0",
volume = "328",
series = "Frontiers in Artificial Intelligence and Applications",
publisher = "IOS Press",
pages = "55--61",
editor = "Andrius Utka and Jurgita Vaicenoniene and Jolanta Kovalevskaite and Danguole Kalinauskaite",
booktitle = "Human Language Technologies - The Baltic Perspective - Proceedings of the 9th International Conference Baltic HLT 2020",
}