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

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

Research output: Contribution to journalConference articlepeer-review

42 Scopus citations

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.

Original languageEnglish
JournalProceedings of the Annual Meeting of the Association for Computational Linguistics
StatePublished - 2020
Externally publishedYes
Event1st Workshop on NLP for COVID-19 at the 58th Annual Meeting of the Association for Computational Linguistics, ACL 2020 - Virtual, Online, United States
Duration: 1 Jul 2020 → …

Fingerprint

Dive into the research topics of 'Cross-language sentiment analysis of European Twitter messages during the COVID-19 pandemic'. Together they form a unique fingerprint.

Cite this