A review on Natural Language Processing Models for COVID-19 research

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).]

Publication DOI: https://doi.org/10.1016/j.health.2022.100078
Divisions: College of Business and Social Sciences > Aston Business School > Operations & Information Management
College of Business and Social Sciences > Aston Business School
Additional Information: 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/)
Uncontrolled Keywords: Natural Language Processing,Sentiment analysis,Machine learning,COVID-19,Transformer models
Publication ISSN: 2772-4425
Last Modified: 30 Sep 2024 13:32
Date Deposited: 21 Aug 2023 15:48
Full Text Link:
Related URLs: https://www.sci ... 772442522000326 (Publisher URL)
PURE Output Type: Article
Published Date: 2022-11
Published Online Date: 2022-07-19
Accepted Date: 2022-07-12
Submitted Date: 2022-05-22
Authors: Hall, Karl
Chang, Victor (ORCID Profile 0000-0002-8012-5852)
Jayne, Chrisina

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