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Exploring the Non-Medical impacts of Covid-19 using Natural Language Processing

by Amol Agade, Samta Balpande
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 175 - Number 36
Year of Publication: 2020
Authors: Amol Agade, Samta Balpande
10.5120/ijca2020920923

Amol Agade, Samta Balpande . Exploring the Non-Medical impacts of Covid-19 using Natural Language Processing. International Journal of Computer Applications. 175, 36 ( Dec 2020), 16-23. DOI=10.5120/ijca2020920923

@article{ 10.5120/ijca2020920923,
author = { Amol Agade, Samta Balpande },
title = { Exploring the Non-Medical impacts of Covid-19 using Natural Language Processing },
journal = { International Journal of Computer Applications },
issue_date = { Dec 2020 },
volume = { 175 },
number = { 36 },
month = { Dec },
year = { 2020 },
issn = { 0975-8887 },
pages = { 16-23 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume175/number36/31684-2020920923/ },
doi = { 10.5120/ijca2020920923 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-07T00:40:51.298995+05:30
%A Amol Agade
%A Samta Balpande
%T Exploring the Non-Medical impacts of Covid-19 using Natural Language Processing
%J International Journal of Computer Applications
%@ 0975-8887
%V 175
%N 36
%P 16-23
%D 2020
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Ongoing COVID-19 Pandemic has resulted into massive damage to various platforms of global economy which has caused disruption to human livelihood. Natural Language Processing has been extensively used in different organizations to categorize sentiments, perform recommendation, summarizing information and topic modelling. This research aims to understand the non-medical impact of COVID-19 on global economy by leveraging the natural language processing methodology. This methodology comprises of text classification which includes topic modelling on unstructured COVID-19 media articles dataset provided by Anacode. Like other Natural Language Processing algorithms, Latent Dirichlet allocation (LDA) and Non-negative matrix factorization (NMF) has been proposed to classify the media articles dataset in order to analyze COVID-19 pandemic impacts in the different sectors of global economy. Model Accuracy was examined based on the coherence and perplexity score which came out to be 0.51 and -10.90 using LDA algorithm. Both the LDA and NMF algorithm identified similar prevalent topics that was impacted by COVID-19 pandemic in multiple sectors of economy. Through intertopic distance map visualization produced by LDA algorithm, it can be reciprocated that general industries which includes children schooling, parental care, and family gatherings had the major impact followed by business sector and the financial industry.

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Index Terms

Computer Science
Information Sciences

Keywords

COVID-19 Deep Learning Natural Language Processing Topic Modelling Text Classification Latent Dirichlet allocation (LDA) Non-negative matrix factorization (NMF).

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