Abstract
This survey paper reviews Natural Language Processing Models and their use in COVID-19 research in two main areas. Firstly, a range of transformer-based biomedical pretrained language models are evaluated using the BLURB benchmark. Secondly, models used in sentiment analysis surrounding COVID-19 vaccination are evaluated. We filtered literature curated from various repositories such as PubMed and Scopus and reviewed 27 papers. When evaluated using the BLURB benchmark, the novel T-BPLM BioLinkBERT gives groundbreaking results by incorporating document link knowledge and hyperlinking into its pretraining. Sentiment analysis of COVID-19 vaccination through various Twitter API tools has shown the public's sentiment towards vaccination to be mostly positive. Finally, we outline some limitations and potential solutions to drive the research community to improve the models used for NLP tasks. [Abstract copyright: © 2022 The Author(s).]
| Original language | English |
|---|---|
| Article number | 100078 |
| Number of pages | 11 |
| Journal | Healthcare analytics (New York, N.Y.) |
| Volume | 2 |
| Early online date | 19 Jul 2022 |
| DOIs | |
| Publication status | Published - Nov 2022 |
Bibliographical note
Copyright © 2022 The Author(s). Published by Elsevier Inc. This is an open access article under the CC BY-NC-ND license(http://creativecommons.org/licenses/by-nc-nd/4.0/)
Keywords
- Natural Language Processing
- Sentiment analysis
- Machine learning
- COVID-19
- Transformer models