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The COVID-19 pandemic has caused a global health crisis that affects manyaspects of human lives. In the absence of vaccines and antivirals, severalbehavioural change and policy initiatives, such as physical distancing, havebeen implemented to control the spread of the coronavirus. Social media datacan reveal public perceptions toward how governments and health agencies acrossthe globe are handling the pandemic, as well as the impact of the disease onpeople regardless of their geographic locations in line with various factorsthat hinder or facilitate the efforts to control the spread of the pandemicglobally. This paper aims to investigate the impact of the COVID-19 pandemic onpeople globally using social media data. We apply natural language processing(NLP) and thematic analysis to understand public opinions, experiences, andissues with respect to the COVID-19 pandemic using social media data. First, wecollect over 47 million COVID-19-related comments from Twitter, Facebook,YouTube, and three online discussion forums. Second, we perform datapreprocessing which involves applying NLP techniques to clean and prepare thedata for automated theme extraction. Third, we apply context-aware NLP approachto extract meaningful keyphrases or themes from over 1 million randomlyselected comments, as well as compute sentiment scores for each theme andassign sentiment polarity based on the scores using lexicon-based technique.Fourth, we categorize related themes into broader themes. A total of 34negative themes emerged, out of which 15 are health-related issues,psychosocial issues, and social issues related to the COVID-19 pandemic fromthe public perspective. In addition, 20 positive themes emerged from ourresults. Finally, we recommend interventions that can help address the negativeissues based on the positive themes and other remedial ideas rooted inresearch.

Health, Psychosocial, and Social issues emanating from COVID-19 pandemic based on Social Media Comments using Natural Language Processing