Robustness of Sentiment Analysis of Multilingual Twitter Postings

Due to increasing digitalisation and access to content published online, the amount of data continues to grow. Opinions, experiences and thoughts are shared on various online platforms. In particular, sharing personal content on social media has become increasingly popular in recent years. Mostly, microblogging is done on social media using text. This text data can be further processed. Information can be extracted from the posted text. Often, a so-called sentiment analysis is used, to determine whether texts adopt a positive, neutral, or negative attitude. Such analysis can be relevant for politics, marketing or economics. Whenever text of a different origin language is to be analysed, a translation to English has to be made beforehand, since sentiment analyses are primarily designed for English text. The necessity for translation poses the question of an introduction of bias towards a particular sentiment, through machine translation. This work shows that for two different architectures of transformer network-based translations, only minimal changes are detectable. This is proved with examples from different origin languages.