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.
@article{arxiv.2008.10022,
title = {COVID-19 Pandemic: Identifying Key Issues using Social Media and Natural Language Processing},
author = {Oladapo Oyebode and Chinenye Ndulue and Dinesh Mulchandani and Banuchitra Suruliraj and Ashfaq Adib and Fidelia Anulika Orji and Evangelos Milios and Stan Matwin and Rita Orji},
journal= {arXiv preprint arXiv:2008.10022},
year = {2022}
}