Published September 13, 2022 | Version Preprint
Journal article Open

Covid-twitter-bert: A natural language processing model to analyse covid-19 content on twitter

  • 1. Digital Epidemiology Lab, EPFL
  • 2. FISABIO-Public HealthIn 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 from social media

Description

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 from social media

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Funding

European Commission
VACMA - Vaccine Media Analytics 797876