About: COVID-19 Pandemic: Identifying Key Issues using Social Media and Natural Language Processing

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An Entity of Type : schema:ScholarlyArticle, within Data Space : covidontheweb.inria.fr associated with source document(s)

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type	Academic Article research paper schema:ScholarlyArticle
isDefinedBy	Covid-on-the-Web dataset
title	COVID-19 Pandemic: Identifying Key Issues using Social Media and Natural Language Processing
Creator	Adib, Ashfaq Matwin, Stan Milios, Evangelos Mulchandani, Dinesh Ndulue, Chinenye »more»
source	ArXiv
abstract	The COVID-19 pandemic has affected people's lives in many ways. Social media data can reveal public perceptions and experience with respect to the pandemic, and also reveal factors that hamper or support efforts to curb global spread of the disease. In this paper, we analyzed COVID-19-related comments collected from six social media platforms using Natural Language Processing (NLP) techniques. We identified relevant opinionated keyphrases and their respective sentiment polarity (negative or positive) from over 1 million randomly selected comments, and then categorized them into broader themes using thematic analysis. Our results uncover 34 negative themes out of which 17 are economic, socio-political, educational, and political issues. 20 positive themes were also identified. We discuss the negative issues and suggest interventions to tackle them based on the positive themes and research evidence.
has issue date	2020-08-23(xsd:dateTime)
has license	arxiv
sha1sum (hex)	4e130a645a8020ee4ae80a779a956c8ba92ae540
resource representing a document's title	COVID-19 Pandemic: Identifying Key Issues using Social Media and Natural Language Processing
resource representing a document's body	covid:4e130a645a8020ee4ae80a779a956c8ba92ae540#body_text
is schema:about of	named entity 'socio-political' named entity 'factors' named entity 'Natural Language Processing (NLP)' named entity 'COVID-19' named entity 'INTERVENTIONS' »more»

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