Using A Probabilistic Topic Model to Link Observers' Perception Tendency to Personality

Abstract

Targeting multiparty conversations, the present study aims to elucidate how an observer will tend to perceive others' emotional states; develops a computational model that realizes the automatic inferencing of the observer's perception tendency. This paper proposes a probabilistic model that automatically discovers the correlation between perception tendency, gender, and personality traits of a target observer. Perception tendency, a probability distribution, explains how likely the observer is to perceive a certain state/level of a target emotion. Personality traits are measured by a variety of questionnaires. The proposed model links these three factors via a latent variable and explains observer's characteristics as a mixture of prototypical characters. An experiment is conducted with fifty observers. They watch 97 short conversation videos and give their impressions about the empathy between each interacting pair. The results demonstrate that the proposed method can find a reasonable framework that underlies the factors: e.g. 1) people who have high scores in Davis's empathy measures show empathy-biased response tendency, and 2) people who have strong sense of consideration for others tend to show an extreme response tendency, and such people are likely to be females. The proposed method shows promise in estimating an observer's perception tendency from his/her gender and personality traits, even when the target perception tendency is quite different from the average perception tendency among observers.

References

Conferences

S. Kumano, K. Otsuka, M. Matsuda, R. Ishii, J. Yamato, "Using A Probabilistic Topic Model to Link Observers' Perception Tendency to Personality", Humaine Association Conference on Affective Computing and Intelligent Interaction (ACII), 2013. [pdf]