# Sentiment Analysis and Text Analysis of the Public Discourse on Twitter about COVID-19 and MPox

arXiv: https://arxiv.org/abs/2312.10580 · HTML: https://mlsanity.com/paper/2312.10580/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 144 references that are in the map.
- First posted on arXiv 17 Dec 2023.

## Builds on (most cited first, 10 of 10)

- [#RoeOverturned: Twitter Dataset on the Abortion Rights Controversy](https://mlsanity.com/paper/2302.01439/) (2023)
- [Detecting Community Depression Dynamics Due to COVID-19 Pandemic in Australia](https://mlsanity.com/paper/2007.02325/) (2020)
- [A Large-Scale Dataset of Twitter Chatter about Online Learning during the Current COVID-19 Omicron Wave](https://mlsanity.com/paper/2208.07810/) (2022)
- [Mood of India During Covid-19 -- An Interactive Web Portal Based on Emotion Analysis of Twitter Data](https://mlsanity.com/paper/2005.02955/) (2020)
- [Examination of Community Sentiment Dynamics due to COVID-19 Pandemic: A Case Study from A State in Australia](https://mlsanity.com/paper/2006.12185/) (2020)
- [A Twitter narrative of the COVID-19 pandemic in Australia](https://mlsanity.com/paper/2302.11136/) (2023)
- [A CNN-LSTM-based hybrid deep learning approach to detect sentiment polarities on Monkeypox tweets](https://mlsanity.com/paper/2208.12019/) (2022)
- [Detecting and monitoring foodborne illness outbreaks: Twitter communications and the 2015 U.S. Salmonella outbreak linked to imported cucumbers](https://mlsanity.com/paper/1708.07534/) (2017)
- [COVID-19: Social Media Sentiment Analysis on Reopening](https://mlsanity.com/paper/2006.00804/) (2020)
- [Global Sentiment Analysis Of COVID-19 Tweets Over Time](https://mlsanity.com/paper/2010.14234/) (2020)

## Built on by (most cited first, 8 of 8)

- [Analyzing Public Reactions, Perceptions, and Attitudes during the MPox Outbreak: Findings from Topic Modeling of Tweets](https://mlsanity.com/paper/2312.11895/) (2023)
- [COVID-19 on YouTube: A Data-Driven Analysis of Sentiment, Toxicity, and Content Recommendations](https://mlsanity.com/paper/2412.17180/) (2024)
- [A Labelled Dataset for Sentiment Analysis of Videos on YouTube, TikTok, and Other Sources about the 2024 Outbreak of Measles](https://mlsanity.com/paper/2406.07693/) (2024)
- [Quantifying Public Response to COVID-19 Events: Introducing the Community Sentiment and Engagement Index](https://mlsanity.com/paper/2412.16925/) (2024)
- [Systematic Classification of Studies Investigating Social Media Conversations about Long COVID Using a Novel Zero-Shot Transformer Framework](https://mlsanity.com/paper/2503.11845/) (2025)
- [Comparative sentiment analysis of public perception: Monkeypox vs. COVID-19 behavioral insights](https://mlsanity.com/paper/2505.07430/) (2025)
- [Content and Engagement Trends in COVID-19 YouTube Videos: Evidence from the Late Pandemic](https://mlsanity.com/paper/2509.01954/) (2025)
- [Automated Multilabel Mpox Research Classification with Explainable Transformer Models](https://mlsanity.com/paper/2607.26700/) (2026)

## Abstract (arXiv)

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.