  In the realm of public health, vaccination stands as the cornerstone for
mitigating disease risks and controlling their proliferation. The recent
COVID-19 pandemic has highlighted how vaccines play a crucial role in keeping
us safe. However the situation involves a mix of perspectives, with skepticism
towards vaccines prevailing for various reasons such as political dynamics,
apprehensions about side effects, and more. The paper addresses the challenge
of comprehensively understanding and categorizing these diverse concerns
expressed in the context of vaccination. Our focus is on developing a robust
multi-label classifier capable of assigning specific concern labels to tweets
based on the articulated apprehensions towards vaccines. To achieve this, we
delve into the application of a diverse set of advanced natural language
processing techniques and machine learning algorithms including transformer
models like BERT, state of the art GPT 3.5, Classifier Chains & traditional
methods like SVM, Random Forest, Naive Bayes. We see that the cutting-edge
large language model outperforms all other methods in this context.
