Personality classification and behaviour interpretation: an approach based on feature categories

Abstract

This paper focuses on recognizing and understanding social dimensions (the personality traits and social impressions) during small group interactions. We extract a set of audio and visual features, which are divided into three categories: intra-personal features (i.e. related to only one participant), dyadic features (i.e. related to a pair of participants) and one vs all features (i.e. related to one participant versus the other members of the group). First, we predict the personality traits (PT) and social impressions (SI) by using these three feature categories. Then, we analyse the interplay be- tween groups of features and the personality traits/social impressions of the interacting participants. The prediction is done by using Support Vector Machine and Ridge Regression which allows to determine the most dominant features for each social dimension. Our experiments show that the combination of intra-personal and one vs all features can greatly improve the prediction accuracy of personality traits and social impressions. Prediction accuracy reaches 81.37% for the social impression named ’Rank of Dominance’. Finally, we draw some interesting conclusions about the relationship between personality traits/social impressions and social features.