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

arXiv: https://arxiv.org/abs/2010.12401 · HTML: https://mlsanity.com/paper/2010.12401/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 23 Oct 2020.

## Built on by (most cited first, 4 of 4)

- [Annotations for Exploring Food Tweets From Multiple Aspects](https://mlsanity.com/paper/2412.06179/) (2024)
- [Transparent AI for Mathematics: Transformer-Based Large Language Models for Mathematical Entity Relationship Extraction with XAI](https://mlsanity.com/paper/2603.06348/) (2026)
- [Fragmented and Valuable: Following Sentiment Changes in Food Tweets](https://mlsanity.com/paper/2106.04903/) (2021)
- [Automated Multilabel Mpox Research Classification with Explainable Transformer Models](https://mlsanity.com/paper/2607.26700/) (2026)

## Abstract (arXiv)

In this paper, we present various pre-training strategies that aid in im-proving the accuracy of the sentiment classification task. We, at first, pre-trainlanguage representation models using these strategies and then fine-tune them onthe downstream task. Experimental results on a time-balanced tweet evaluation setshow the improvement over the previous technique. We achieve 76% accuracy forsentiment analysis on Latvian tweets, which is a substantial improvement over pre-vious work