| Autor: |
Wang, Shihan, Schraagen, Marijn, Tjong Kim Sang, Erik, Dastani, Mehdi, Verspoor, Karin, Bretonnel Cohen, Kevin, Conway, Michael, de Bruijn, Berry, Dredze, Mark, Mihalcea, Rada, Wallace, Byron |
| Přispěvatelé: |
Sub Intelligent Systems, Sub Natural Language Processing, ILS Variation, Intelligent Systems |
| Jazyk: |
angličtina |
| Rok vydání: |
2020 |
| Popis: |
Public sentiment (the opinion, attitude or feeling that the public expresses) is a factor of interest for government, as it directly influences the implementation of policies. Given the unprecedented nature of the COVID-19 crisis, having an up-to-date representation of public sentiment on governmental measures and announcements is crucial. In this paper, we analyse Dutch public sentiment on governmental COVID-19 measures from text data collected across three online media sources (Twitter, Reddit and Nu.nl) from February to September 2020. We apply sentiment analysis methods to analyse polarity over time, as well as to identify stance towards two specific pandemic policies regarding social distancing and wearing face masks. The presented preliminary results provide valuable insights into the narratives shown in vast social media text data, which help understand the influence of COVID-19 measures on the general public. |
| Databáze: |
OpenAIRE |
| Externí odkaz: |
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