Sentiment Analysis and Text Analysis of the Public Discourse on Twitter
  about COVID-19 and MPox
Nirmalya Thakur
Mining and analysis of the big data of Twitter conversations have been of
significant interest to the scientific community in the fields of healthcare,
epidemiology, big data, data science, computer science, and their related
areas, as can be seen from several works in the last few years that focused on
sentiment analysis and other forms of text analysis of tweets related to Ebola,
E-Coli, Dengue, Human Papillomavirus, Middle East Respiratory Syndrome,
Measles, Zika virus, H1N1, influenza like illness, swine flu, flu, Cholera,
Listeriosis, cancer, Liver Disease, Inflammatory Bowel Disease, kidney disease,
lupus, Parkinsons, Diphtheria, and West Nile virus. The recent outbreaks of
COVID-19 and MPox have served as catalysts for Twitter usage related to seeking
and sharing information, views, opinions, and sentiments involving both of
these viruses. None of the prior works in this field analyzed tweets focusing
on both COVID-19 and MPox simultaneously. To address this research gap, a total
of 61,862 tweets that focused on MPox and COVID-19 simultaneously, posted
between 7 May 2022 and 3 March 2023, were studied. The findings and
contributions of this study are manifold. First, the results of sentiment
analysis using the VADER approach show that nearly half the tweets had a
negative sentiment. It was followed by tweets that had a positive sentiment and
tweets that had a neutral sentiment, respectively. Second, this paper presents
the top 50 hashtags used in these tweets. Third, it presents the top 100 most
frequently used words in these tweets after performing tokenization, removal of
stopwords, and word frequency analysis. Finally, a comprehensive comparative
study that compares the contributions of this paper with 49 prior works in this
field is presented to further uphold the relevance and novelty of this work.