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.
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