Analysis of Twitter Content Related to Covid-19 Vaccination Using NLP

Pamela Vogli

Bachelor's thesis

Abstract:

The world was introduced to the COVID-19 virus in December 2019, which caused widespread concern and devastation. The development of a vaccine was seen as the most promising solution to the problem, leading to extensive research by organizations worldwide. However, the discovery of a significant number of people who were hesitant to take the vaccine prompted further investigation. This study aims to examine these hesitancy levels by analyzing tweets on Twitter. A sample of a substantial number of tweets was collected between January 2020 and January 2021. The tweets were subjected to natural language processing (NLP) based on sentiment and statistical analysis. The results provide valuable insights into the attitudes and opinions of people regarding COVID-19 vaccination. The study also highlights the importance of understanding the public's perception of vaccines and the need for effective communication strategies to address vaccine hesitancy. In conclusion, the analysis of Twitter content related to COVID-19 vaccination provides a unique perspective on the public's perception of vaccines and contributes to the ongoing discussion about vaccine hesitancy.

Keywords

Vaccine, Covid-19, Social media, Twitter, Hashtags, Tweets, Content analysis, NLP

Jazyk práce: English

Datum vytvoření / odevzdání či podání práce: 16. 3. 2023

Obhajoba závěrečné práce

• Obhajoba proběhla 17. 5. 2023
• Vedúci: Ing. Miloš Ulman, Ph.D.
• Oponent: John Phillip Sabou, Ph.D.

Czech University of Life Sciences Prague

Faculty of Economics and Management

Bachelor programme:
Informatics