COVID-Twitter-BERT: A Natural Language Processing Model to Analyse COVID-19 Content on Twitter

• URL: http://arxiv.org/abs/2005.07503v1
• Date: Fri, 15 May 2020 12:40:46 GMT
• Title: COVID-Twitter-BERT: A Natural Language Processing Model to Analyse COVID-19 Content on Twitter
• Authors: Martin M\"uller, Marcel Salath\'e, Per E Kummervold
• Abstract summary: We release COVID-Twitter-BERT (CT-BERT), a transformer-based model, pretrained on a large corpus of Twitter messages on the topic of COVID-19. Our model shows a 10-30% marginal improvement compared to its base model, BERT-Large, on five different classification datasets. CT-BERT is optimised to be used on COVID-19 content, in particular social media posts from Twitter.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: In this work, we release COVID-Twitter-BERT (CT-BERT), a transformer-based model, pretrained on a large corpus of Twitter messages on the topic of COVID-19. Our model shows a 10-30% marginal improvement compared to its base model, BERT-Large, on five different classification datasets. The largest improvements are on the target domain. Pretrained transformer models, such as CT-BERT, are trained on a specific target domain and can be used for a wide variety of natural language processing tasks, including classification, question-answering and chatbots. CT-BERT is optimised to be used on COVID-19 content, in particular social media posts from Twitter.

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