Summary
Presented: IEEE AIxSET 2024, Laguna Hills, California, USA The widespread use of social media platforms during the COVID-19 pandemic has highlighted the importance of understanding public sentiment towards vaccines. This paper presents an evaluation of large language models (LLMs), a fine- tuned BERT model, and ensemble methods for analyzing social media posts for vaccine sentiment. We introduce a novel dataset comprising annotated social media posts and evaluate the perfor- mance of various models in sentiment classification. Our results demonstrate that LLMs can accurately classify sentiments, open- source LLMs have approached parity with closed-source LLMs, ensemble models can provide marginal improvements, and fine- tuned SLMs can approach the performance of LLMs at much smaller footprint and running costs, making them feasible in many resource-constrained environments.