  Emotions play a critical role in our everyday lives by altering how we
perceive, process and respond to our environment. Affective computing aims to
instill in computers the ability to detect and act on the emotions of human
actors. A core aspect of any affective computing system is the classification
of a user's emotion. In this study we present a novel methodology for
classifying emotion in a conversation. At the backbone of our proposed
methodology is a pre-trained Language Model (LM), which is supplemented by a
Graph Convolutional Network (GCN) that propagates information over the
predicate-argument structure identified in an utterance. We apply our proposed
methodology on the IEMOCAP and Friends data sets, achieving state-of-the-art
performance on the former and a higher accuracy on certain emotional labels on
the latter. Furthermore, we examine the role context plays in our methodology
by altering how much of the preceding conversation the model has access to when
making a classification.
