  Ordinal Classification (OC) is a widely encountered challenge in Natural
Language Processing (NLP), with applications in various domains such as
sentiment analysis, rating prediction, and more. Previous approaches to tackle
OC have primarily focused on modifying existing or creating novel loss
functions that \textbf{explicitly} account for the ordinal nature of labels.
However, with the advent of Pretrained Language Models (PLMs), it became
possible to tackle ordinality through the \textbf{implicit} semantics of the
labels as well. This paper provides a comprehensive theoretical and empirical
examination of both these approaches. Furthermore, we also offer strategic
recommendations regarding the most effective approach to adopt based on
specific settings.
