A Multivariate Regression Approach to Personality Impression Recognition of Vloggers

Research in psychology has suggested that behavior of individuals can be explained to a great extent by their underlying personality traits. In this paper, we focus on predicting how the personality of YouTube video bloggers is perceived by their viewers. Our approach to personality recognition is multimodal in the sense that we use audio-video features, as well as textual (emotional and linguistic) features extracted from the transcripts of vlogs. Based on these features, we predict the extent to which the video blogger is perceived to exhibit each of the traits of the Big Five personality model. In addition, we explore 5 multivariate regression techniques and contrast them with a single target approach for predicting personality impression scores. All 6 algorithms are able to outperform the average baseline model for all 5 personality traits on a dataset of 404 YouTube videos. This is interesting because previously published methods for the same dataset show an improvement over the baseline for the majority of personality traits, but not for all simultaneously.
to appear in: Proceedings of WCPR14 (Workshop on Computational Personality Recognition), workshop at ACMMM2014 (22nd ACM International Conference on Multimedia)
G. Farnadi, S. Sushmita, G. Sitaraman, N. Ton, M. De Cock, S. Davalos
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