# Comparative sentiment analysis of public perception: Monkeypox vs. COVID-19 behavioral insights

arXiv: https://arxiv.org/abs/2505.07430 · HTML: https://mlsanity.com/paper/2505.07430/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 4 of its 40 references that are in the map.
- First posted on arXiv 12 May 2025.

## Builds on (most cited first, 4 of 4)

- [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019)
- [Temperature check: theory and practice for training models with softmax-cross-entropy losses](https://mlsanity.com/paper/2010.07344/) (2020)
- [Sentiment Analysis and Text Analysis of the Public Discourse on Twitter about COVID-19 and MPox](https://mlsanity.com/paper/2312.10580/) (2023)
- [Fine-tuned Sentiment Analysis of COVID-19 Vaccine-Related Social Media Data: Comparative Study](https://mlsanity.com/paper/2211.15407/) (2022)

## Abstract (arXiv)

The emergence of global health crises, such as COVID-19 and Monkeypox (mpox), has underscored the importance of understanding public sentiment to inform effective public health strategies. This study conducts a comparative sentiment analysis of public perceptions surrounding COVID-19 and mpox by leveraging extensive datasets of 147,475 and 106,638 tweets, respectively. Advanced machine learning models, including Logistic Regression, Naive Bayes, RoBERTa, DistilRoBERTa and XLNet, were applied to perform sentiment classification, with results indicating key trends in public emotion and discourse. The analysis highlights significant differences in public sentiment driven by disease characteristics, media representation, and pandemic fatigue. Through the lens of sentiment polarity and thematic trends, this study offers valuable insights into tailoring public health messaging, mitigating misinformation, and fostering trust during concurrent health crises. The findings contribute to advancing sentiment analysis applications in public health informatics, setting the groundwork for enhanced real-time monitoring and multilingual analysis in future research.