  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.
