Pretraining and Fine-Tuning Strategies for Sentiment Analysis of Latvian Tweets

被引：5

作者：

Thakkar, Gaurish ^{[1
]}

Pinnis, Marcis ^{[2
,3
]}

机构：

[1] Univ Zagreb, Fac Humanities & Social Sci, Ul Ivana Lucica 3, Zagreb 10000, Croatia

[2] Tilde, Vienibas Gatve 75A, LV-1004 Riga, Latvia

[3] Univ Latvia, Raina Bulv 19-125, LV-1586 Riga, Latvia

来源：

HUMAN LANGUAGE TECHNOLOGIES - THE BALTIC PERSPECTIVE (HLT 2020) | 2020年 / 328卷

关键词：

Sentiment analysis; word embeddings; BERT; Latvian;

D O I：

10.3233/FAIA200602

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

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.

引用

页码：55 / 61

页数：7

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