Cross-language sentiment analysis of European Twitter messages during the COVID-19 pandemic

Anna Kruspe, Matthias Haeberle, Iona Kuhn, Xiao Xiang Zhu

Keywords: sentiment analysis, covid-19, twitter, social media, sentence embeddings, europe

TL;DR: In this paper, sentiment in European Twitter messages in the first months of the COVID-19 pandemic is analyzed and correlated with events by country.

Abstract:

Social media data can be a very salient source of information during crises. User-generated messages provide a window into people’s minds during such times, allowing us insights about their moods and opinions. Due to the vast amounts of such messages, a large-scale analysis of population-wide developments becomes possible.
In this paper, we analyze Twitter messages (tweets) collected during the first months of the COVID-19 pandemic in Europe with regard to their sentiment. This is implemented with a neural network for sentiment analysis using multilingual sentence embeddings. We separate the results by country of origin, and correlate their temporal development with events in those countries. This allows us to study the effect of the situation on people’s moods. We see, for example, that lockdown announcements correlate with a deterioration of mood in almost all surveyed countries, which recovers within a short time span.