On the construction of more human-like chatbots: Affect and emotion analysis of movie dialogue data

Abstract

Affect and emotion are inherent properties of human-human communication and interaction. Recent research interest in chatbots and conversational agents aims at making human-machine interaction more human-like in both behavioral and attitudinal terms. This paper intends to present some baby steps in this direction by analyzing a large dialogue dataset in terms of tonal, affective and emotional bias, with the objective of providing a valuable resource for developing and training datadriven conversational agents with discriminative power across such dimensions. Preliminary results of the conducted analysis demonstrate that only a relative small, although not negligible, percentage of the dialogue turns present clear orientation in any of the considered dimensions. Future research is still needed to determine whether this proportion is enough for biasing system responses in order to create different personality trends in conversational agents that are perceptible by humans when interacting with them.