Pieter Delobelle
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README.md
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- English
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- Tweets
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- Sentiment analysis
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# Measuring Shifts in Attitudes Towards COVID-19 Measures in Belgium Using Multilingual BERT
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[Blog post »](https://people.cs.kuleuven.be/~pieter.delobelle/attitudes-towards-covid-19-measures/?utm_source=huggingface&utm_medium=social&utm_campaign=corona_tweets) · [paper »](http://arxiv.org/abs/2104.09947)
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We categorized several months worth of these Tweets by topic (government COVID measure) and opinion expressed. Below is a timeline of the relative number of Tweets on the curfew topic (middle) and the fraction of those Tweets that find the curfew too strict, too loose, or a suitable measure (bottom), with the number of daily cases in Belgium to give context on the pandemic situation (top).
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![chart.png](https://github.com/iPieter/bert-corona-tweets/raw/master/chart.png)
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- English
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- Tweets
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- Sentiment analysis
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widget:
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- text: "I really wish I could leave my house after midnight, this makes no sense!"
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# Measuring Shifts in Attitudes Towards COVID-19 Measures in Belgium Using Multilingual BERT
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[Blog post »](https://people.cs.kuleuven.be/~pieter.delobelle/attitudes-towards-covid-19-measures/?utm_source=huggingface&utm_medium=social&utm_campaign=corona_tweets) · [paper »](http://arxiv.org/abs/2104.09947)
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This model can be used to determine if a tweet expresses support or not for a curfew. The model was trained on manually labeled tweets from Belgium in Dutch, French and English.
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We categorized several months worth of these Tweets by topic (government COVID measure) and opinion expressed. Below is a timeline of the relative number of Tweets on the curfew topic (middle) and the fraction of those Tweets that find the curfew too strict, too loose, or a suitable measure (bottom), with the number of daily cases in Belgium to give context on the pandemic situation (top).
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![chart.png](https://github.com/iPieter/bert-corona-tweets/raw/master/chart.png)
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