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. 2022 Feb 11;6(2):174-207.
doi: 10.1007/s41666-021-00111-w. eCollection 2022 Jun.

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

Oladapo Oyebode  1 , Chinenye Ndulue  1 , Dinesh Mulchandani  1 , Banuchitra Suruliraj  1 , Ashfaq Adib  1 , Fidelia Anulika Orji  2 , Evangelos Milios  1 , Stan Matwin  1   3 , Rita Orji  1
Affiliations

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

Oladapo Oyebode et al. J Healthc Inform Res. .

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. Twenty (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.

Keywords: COVID-19; Coronavirus; Health informatics; Keyphrase extraction; Natural language processing; Social media; Text mining; Thematic analysis.

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Conflict of interest statement

Conflict of InterestThe authors declare no competing interests.

Figures

Fig. 1
Fig. 1
NLP pipeline for extracting opinionated keyphrases from COVID-19-related comments
Fig. 2
Fig. 2
The KeyphraseExtractor algorithm based on the context-aware NLP approach
Fig. 3
Fig. 3
A sample parse tree illustrating the output of the chunker
Fig. 4
Fig. 4
Sample negative keyphrases and their frequency of occurrence (a larger bubble size illustrates higher dominance)
Fig. 5
Fig. 5
Sample positive keyphrases and their frequency of occurrence (a larger bubble size illustrates higher dominance)
Fig. 6
Fig. 6
The chart shows negative themes and the corresponding number of subthemes
Fig. 7
Fig. 7
The chart shows the total number of user comments associated with each negative theme
Fig. 8
Fig. 8
The chart shows positive themes and the corresponding number of subthemes
Fig. 9
Fig. 9
The chart shows the total number of user comments associated with each positive theme
See this image and copyright information in PMC

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