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

被引：14

作者：

Oyebode, Oladapo ^{[1
]}

Ndulue, Chinenye ^{[1
]}

Mulchandani, Dinesh ^{[1
]}

Suruliraj, Banuchitra ^{[1
]}

Adib, Ashfaq ^{[1
]}

Orji, Fidelia Anulika ^{[2
]}

Milios, Evangelos ^{[1
]}

Matwin, Stan ^{[1
,3
]}

Orji, Rita ^{[1
]}

机构：

[1] Dalhousie Univ, Fac Comp Sci, Halifax, NS B3H 4R2, Canada

[2] Univ Saskatchewan, Dept Comp Sci, Saskatoon, SK S7N 5C9, Canada

[3] Polish Acad Sci, Inst Comp Sci, Warsaw, Poland

来源：

JOURNAL OF HEALTHCARE INFORMATICS RESEARCH | 2022年 / 6卷 / 02期

基金：

加拿大自然科学与工程研究理事会;

关键词：

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

D O I：

10.1007/s41666-021-00111-w

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

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.

引用

页码：174 / 207

页数：34

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