  Negotiation is a complex social interaction that encapsulates emotional
encounters in human decision-making. Virtual agents that can negotiate with
humans are useful in pedagogy and conversational AI. To advance the development
of such agents, we explore the prediction of two important subjective goals in
a negotiation - outcome satisfaction and partner perception. Specifically, we
analyze the extent to which emotion attributes extracted from the negotiation
help in the prediction, above and beyond the individual difference variables.
We focus on a recent dataset in chat-based negotiations, grounded in a
realistic camping scenario. We study three degrees of emotion dimensions -
emoticons, lexical, and contextual by leveraging affective lexicons and a
state-of-the-art deep learning architecture. Our insights will be helpful in
designing adaptive negotiation agents that interact through realistic
communication interfaces.
