  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.
