# Improving Sentiment Analysis over non-English Tweets using Multilingual Transformers and Automatic Translation for Data-Augmentation

arXiv: https://arxiv.org/abs/2010.03486 · HTML: https://mlsanity.com/paper/2010.03486/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 Oct 2020.

## Built on by (most cited first, 9 of 9)

- [XLM-T: Multilingual Language Models in Twitter for Sentiment Analysis and Beyond](https://mlsanity.com/paper/2104.12250/) (2021)
- [Data Augmentation Approaches in Natural Language Processing: A Survey](https://mlsanity.com/paper/2110.01852/) (2021)
- [Re-Evaluating GermEval17 Using German Pre-Trained Language Models](https://mlsanity.com/paper/2102.12330/) (2021)
- [Linear Transformations for Cross-lingual Sentiment Analysis](https://mlsanity.com/paper/2209.07244/) (2022)
- [Optimal strategies to perform multilingual analysis of social content for a novel dataset in the tourism domain](https://mlsanity.com/paper/2311.14727/) (2023)
- [Using Machine Translation to Augment Multilingual Classification](https://mlsanity.com/paper/2405.05478/) (2024)
- [Beyond English: Evaluating Automated Measurement of Moral Foundations in Non-English Discourse with a Chinese Case Study](https://mlsanity.com/paper/2502.02451/) (2025)
- [Cross-lingual Aspect-Based Sentiment Analysis: A Survey on Tasks, Approaches, and Challenges](https://mlsanity.com/paper/2508.09516/) (2025)
- [Quantified Facial Expressiveness for Affective Behavior Analytics](https://mlsanity.com/paper/2110.01758/) (2021)

## Abstract (arXiv)

Tweets are specific text data when compared to general text. Although sentiment analysis over tweets has become very popular in the last decade for English, it is still difficult to find huge annotated corpora for non-English languages. The recent rise of the transformer models in Natural Language Processing allows to achieve unparalleled performances in many tasks, but these models need a consequent quantity of text to adapt to the tweet domain. We propose the use of a multilingual transformer model, that we pre-train over English tweets and apply data-augmentation using automatic translation to adapt the model to non-English languages. Our experiments in French, Spanish, German and Italian suggest that the proposed technique is an efficient way to improve the results of the transformers over small corpora of tweets in a non-English language.