Analysis of Covid-19 Literature Evolution via NLP and Machine Learning

The ongoing pandemic has impacted the world order. It has changed people's perceptions and their behaviors towards the seemingly never-ending pandemic of coronavirus. A huge amount of literature is available on SARS-COV2. Research on Covid-19 continues to expand in terms of the involved risk fa...

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Bibliographic Details
Published in2022 International Conference on Recent Advances in Electrical Engineering & Computer Sciences (RAEE & CS) pp. 1 - 8
Main Authors Saif, Mahrukh, Raja, Muhammad Asif Zahoor, Zameer, Aneela
Format Conference Proceeding
LanguageEnglish
Published IEEE 18.10.2022
Subjects
Biological system modeling
COVID -19
LDA
LSTM
Machine learning
Multi-class Classification
Natural language processing
NLP
Pandemics
Topic modeling
Vaccines
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Summary:The ongoing pandemic has impacted the world order. It has changed people's perceptions and their behaviors towards the seemingly never-ending pandemic of coronavirus. A huge amount of literature is available on SARS-COV2. Research on Covid-19 continues to expand in terms of the involved risk factors, disease prediction, diagnostics, pharmaceutical intervention, disease transmission, vaccine creation, impacts on the economy, education, healthcare, and so forth. This study aims to analyze the current literature trend of domain topics most affected by the pandemic and the regions most impacted. The data is collected from various Journals and the COVID-WHO database from the time-span of Jan 2020 - Sep 2021. For binary classification, the Covid-19 specific literature is filtered using LSTM and several machine learning models. Further Covid-related information can be extracted from Covid-related publications on vaccination and prediction of such cases in various regions.
DOI:10.1109/RAEECS56511.2022.9954587