Cross-language sentiment analysis of European Twitter messages duringthe COVID-19 pandemic
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Abstract (arXiv)
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.
Key facts. Cited by 11 papers in the map (data through 7 Oct 2026). First posted on arXiv 27 Aug 2020.
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Citing papers per year: 2021: 2; 2022: 5; 2023: 3; 2024: 1.
11 papers.
- 2023Vision-Language Models in Remote Sensing: Current Progress and Future Trendscited by 93
- 2024Cross-Modal Bidirectional Interaction Model for Referring Remote Sensing Image Segmentationcited by 26
- 2021COVID-19 sentiment analysis via deep learning during the rise of novel casescited by 18
- 2022Using Twitter Data to Understand Public Perceptions of Approved versus Off-label Use for COVID-19-related Medicationscited by 8
- 2022A Dataset and BERT-based Models for Targeted Sentiment Analysis on Turkish Textscited by 7
- 2022Geo-Information Harvesting from Social Media Datacited by 5
- 2023An analysis of vaccine-related sentiments from development to deployment of COVID-19 vaccinescited by 5
- 2022BERT-Deep CNN: State-of-the-Art for Sentiment Analysis of COVID-19 Tweetscited by 2
- 2023Applications and Challenges of Sentiment Analysis in Real-life Scenarioscited by 2
- 2021A Comparative Study on Transfer Learning and Distance Metrics in Semantic Clustering over the COVID-19 Tweetscited by 1
- 2022A Commonsense-Infused Language-Agnostic Learning Framework for Enhancing Prediction of Political Polarity in Multilingual News Headlinescited by 1
- 2024Cross-Modal Bidirectional Interaction Model for Referring Remote Sensing Image Segmentation11 Oct 2024
- 2023An analysis of vaccine-related sentiments from development to deployment of COVID-19 vaccines23 Jun 2023
- 2023Vision-Language Models in Remote Sensing: Current Progress and Future Trends9 May 2023
- 2023Applications and Challenges of Sentiment Analysis in Real-life Scenarios24 Jan 2023
- 2022A Commonsense-Infused Language-Agnostic Learning Framework for Enhancing Prediction of Political Polarity in Multilingual News Headlines1 Dec 2022
- 2022BERT-Deep CNN: State-of-the-Art for Sentiment Analysis of COVID-19 Tweets4 Nov 2022
- 2022Geo-Information Harvesting from Social Media Data1 Nov 2022
- 2022Using Twitter Data to Understand Public Perceptions of Approved versus Off-label Use for COVID-19-related Medications29 Jun 2022
- 2022A Dataset and BERT-based Models for Targeted Sentiment Analysis on Turkish Texts9 May 2022
- 2021A Comparative Study on Transfer Learning and Distance Metrics in Semantic Clustering over the COVID-19 Tweets16 Nov 2021
- 2021COVID-19 sentiment analysis via deep learning during the rise of novel cases5 Apr 2021