# Sentiment Analysis and Topic Modeling for COVID-19 Vaccine Discussions

arXiv: https://arxiv.org/abs/2111.04415 · HTML: https://mlsanity.com/paper/2111.04415/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 30 references that are in the map.
- First posted on arXiv 8 Oct 2021.

## Builds on (most cited first, 7 of 7)

- [Assessing the risks of "infodemics" in response to COVID-19 epidemics](https://mlsanity.com/paper/2004.03997/) (2020)
- [Detecting Topic and Sentiment Dynamics Due to COVID-19 Pandemic Using Social Media](https://mlsanity.com/paper/2007.02304/) (2020)
- [Model Generalization on COVID-19 Fake News Detection](https://mlsanity.com/paper/2101.03841/) (2021)
- [#lockdown: network-enhanced emotional profiling at the times of COVID-19](https://mlsanity.com/paper/2005.04404/) (2020)
- [Detecting Community Depression Dynamics Due to COVID-19 Pandemic in Australia](https://mlsanity.com/paper/2007.02325/) (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)
- [Analyzing COVID-19 on Online Social Media: Trends, Sentiments and Emotions](https://mlsanity.com/paper/2005.14464/) (2020)

## Built on by (most cited first, 7 of 7)

- [A Rapid Review of Clustering Algorithms](https://mlsanity.com/paper/2401.07389/) (2024)
- [Exploring the Power of Topic Modeling Techniques in Analyzing Customer Reviews: A Comparative Analysis](https://mlsanity.com/paper/2308.11520/) (2023)
- [The Geometric Structure of Topic Models](https://mlsanity.com/paper/2403.03607/) (2024)
- [Unveiling the Potential of BERTopic for Multilingual Fake News Analysis -- Use Case: Covid-19](https://mlsanity.com/paper/2407.08417/) (2024)
- [Analyzing COVID-19 Vaccination Sentiments in Nigerian Cyberspace: Insights from a Manually Annotated Twitter Dataset](https://mlsanity.com/paper/2401.13133/) (2024)
- [A Reproducible Framework for Neural Topic Modeling in Focus Group Analysis](https://mlsanity.com/paper/2511.18843/) (2025)
- [BERTopic-Virality Prioritisation: A Scalable Framework for Thematic and Comparative Analysis of COVID-19 and Monkeypox Misinformation on Twitter](https://mlsanity.com/paper/2608.15691/) (2026)

## Abstract (arXiv)

The outbreak of the novel Coronavirus Disease 2019 (COVID-19) has lasted for nearly two years and caused unprecedented impacts on people's daily life around the world. Even worse, the emergence of the COVID-19 Delta variant once again puts the world in danger. Fortunately, many countries and companies have started to develop coronavirus vaccines since the beginning of this disaster. Till now, more than 20 vaccines have been approved by the World Health Organization (WHO), bringing light to people besieged by the pandemic. The promotion of COVID-19 vaccination around the world also brings a lot of discussions on social media about different aspects of vaccines, such as efficacy and security. However, there does not exist much research work to systematically analyze public opinion towards COVID-19 vaccines. In this study, we conduct an in-depth analysis of tweets related to the coronavirus vaccine on Twitter to understand the trending topics and their corresponding sentimental polarities regarding the country and vaccine levels. The results show that a majority of people are confident in the effectiveness of vaccines and are willing to get vaccinated. In contrast, the negative tweets are often associated with the complaints of vaccine shortages, side effects after injections and possible death after being vaccinated. Overall, this study exploits popular NLP and topic modeling methods to mine people's opinions on the COVID-19 vaccines on social media and to analyse and visualise them objectively. Our findings can improve the readability of the noisy information on social media and provide effective data support for the government and policy makers.