Abstract
We present a COVID-19 news dashboard which visualizes sentiment in pandemic news coverage in different languages across Europe. The dashboard shows analyses for positive/neutral/negative sentiment and moral sentiment for news articles across countries and languages. First we extract news articles from news-crawl. Then we use a pre-trained multilingual BERT model for sentiment analysis of news article headlines and a dictionary and word vectors -based method for moral sentiment analysis of news articles. The resulting dashboard gives a unified overview of news events on COVID-19 news overall sentiment, and the region and language of publication from the period starting from the beginning of January 2020 to the end of January 2021.
| Original language | English |
|---|---|
| Title of host publication | EACL Hackashop on News Media Content Analysis and Automated Report Generation : Proceedings |
| Editors | Hannu Toivonen, Michele Boggia |
| Number of pages | 6 |
| Place of Publication | Stroudsburg |
| Publisher | Association for Computational Linguistics (ACL) |
| Publication date | 2021 |
| Pages | 110-115 |
| ISBN (Electronic) | 978-1-954085-13-8 |
| Publication status | Published - 2021 |
| MoE publication type | B3 Article in conference proceedings |
| Event | Conference of the European Chapter of the Association for Computational Linguistics - Virtual, online Duration: 19 Apr 2021 → 19 Apr 2021 Conference number: 16 https://2021.eacl.org/ |
Fields of Science
- 113 Computer and information sciences
- 518 Media and communications