  Autonomous agents that can engage in social interactions witha human is the
ultimate goal of a myriad of applications. A keychallenge in the design of
these applications is to define the socialbehavior of the agent, which requires
extensive content creation.In this research, we explore how we can leverage
current state-of-the-art tools to make inferences about the emotional state ofa
character in a story as events unfold, in a coherent way. Wepropose a character
role-labelling approach to emotion tracking thataccounts for the semantics of
emotions. We show that by identifyingactors and objects of events and
considering the emotional stateof the characters, we can achieve better
performance in this task,when compared to end-to-end approaches.
