@inproceedings{kruspe-etal-2020-cross,
title = "Cross-language sentiment analysis of {European} {Twitter} messages during the {COVID-19} pandemic",
author = {Kruspe, Anna and
H{\"a}berle, Matthias and
Kuhn, Iona and
Zhu, Xiao Xiang},
editor = "Verspoor, Karin and
Cohen, Kevin Bretonnel and
Dredze, Mark and
Ferrara, Emilio and
May, Jonathan and
Munro, Robert and
Paris, Cecile and
Wallace, Byron",
booktitle = "Proceedings of the 1st Workshop on {NLP} for {COVID-19} at {ACL} 2020",
month = jul,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2020.nlpcovid19-acl.14",
abstract = "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.",
}
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%0 Conference Proceedings
%T Cross-language sentiment analysis of European Twitter messages during the COVID-19 pandemic
%A Kruspe, Anna
%A Häberle, Matthias
%A Kuhn, Iona
%A Zhu, Xiao Xiang
%Y Verspoor, Karin
%Y Cohen, Kevin Bretonnel
%Y Dredze, Mark
%Y Ferrara, Emilio
%Y May, Jonathan
%Y Munro, Robert
%Y Paris, Cecile
%Y Wallace, Byron
%S Proceedings of the 1st Workshop on NLP for COVID-19 at ACL 2020
%D 2020
%8 July
%I Association for Computational Linguistics
%C Online
%F kruspe-etal-2020-cross
%X 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.
%U https://aclanthology.org/2020.nlpcovid19-acl.14
Markdown (Informal)
[Cross-language sentiment analysis of European Twitter messages during the COVID-19 pandemic](https://aclanthology.org/2020.nlpcovid19-acl.14) (Kruspe et al., NLP-COVID19 2020)
ACL