  We present a novel deep learning-based framework to generate embedding
representations of fine-grained emotions that can be used to computationally
describe psychological models of emotions. Our framework integrates a
contextualized embedding encoder with a multi-head probing model that enables
to interpret dynamically learned representations optimized for an emotion
classification task. Our model is evaluated on the Empathetic Dialogue dataset
and shows the state-of-the-art result for classifying 32 emotions. Our layer
analysis can derive an emotion graph to depict hierarchical relations among the
emotions. Our emotion representations can be used to generate an emotion wheel
directly comparable to the one from Plutchik's\LN model, and also augment the
values of missing emotions in the PAD emotional state model.
