Cross-language sentiment analysis of European Twitter messages duringthe COVID-19 pandemic
This provides insights into public mood during crises for policymakers and researchers, though it is incremental as it applies existing methods to new data.
The paper analyzed Twitter messages across Europe during the early COVID-19 pandemic to assess sentiment, finding that lockdown announcements correlated with a temporary mood deterioration in most countries.
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.