Sentiment Analysis of Twitter Discourse on Omicron Vaccination in the USA Using VADER and BERT

Authors

  • Satvika Marrapu College of Information, University of North Texas, USA
  • Willam Senn College of Business, Tarleton State University, USA
  • Victor Prybutok College of Business, University of North Texas, USA

DOI:

https://doi.org/10.47852/bonviewJDSIS42022441

Keywords:

Omicron vaccination, Twitter data, sentiment analysis, VADER, BERT, NLP, public sentiment

Abstract

Amid the rapidly evolving discussions surrounding the Omicron vaccination, this research leverages data from Twitter, focusing on the USA, from March 2022 to March 2023. Harnessing the capability of the snscrape Python library, a comprehensive dataset of tweets was collated and subsequently subjected to rigorous sentiment analysis techniques. Two primary methodologies were adopted: The Valence Aware Dictionary for Sentiment Reasoning (VADER) and the Bidirectional Encoder Representations from Transformers (BERT) model. The data underwent preprocessing, which included the removal of URLs, HTML tags, mentions, and stop words. Using VADER, the tweets were initially labeled, forming the foundational layer for training the BERT model. Following tokenization, data batching, and model construction, the BERT model was trained and subsequently evaluated. Results illuminated a multifaceted landscape of emotions in discussions related to the Omicron vaccination during the study period. Furthermore, a discernible relationship was identified, highlighting the emotional flux in vaccine-related Twitter dialogues throughout the Omicron period. This study provides valuable insights into public sentiment during a crucial juncture of the pandemic and underscores the potential of contemporary NLP tools in gauging public opinion.

 

Received: 9 January 2024 | Revised: 12 March 2024 | Accepted: 28 March 2024

 

Conflicts of Interest

The authors declare that they have no conflicts of interest to this work.

 

Data Availability Statement

Data sharing is not applicable to this article as no new data were created or analyzed in this study.

 

Author Contribution Statement

Satvika Marrapu: Conceptualization, Methodology, Software, Formal analysis, Investigation, Resources, Data curation, Writing - original draft, Writing - review & editing, Visualization, Project administration. William Senn: Conceptualization, Validation, Writing - review & editing, Supervision, Project administration. Victor Prybutok: Conceptualization, Methodology, Validation, Writing - review & editing, Supervision.


Downloads

Published

2024-03-29

Issue

Section

Research Articles

How to Cite

Marrapu, S., Senn, W., & Prybutok, V. (2024). Sentiment Analysis of Twitter Discourse on Omicron Vaccination in the USA Using VADER and BERT. Journal of Data Science and Intelligent Systems. https://doi.org/10.47852/bonviewJDSIS42022441