Predicting personality traits using multimodal information

Measuring personality traits has a long story in psychology where analysis has been done by asking sets of questions. These question sets (inventories) have been designed by in- vestigating lexical terms that we use in our daily commu- nications or by analyzing biological phenomena. Whether consciously or unconsciously we express our thoughts and behaviors when communicating with others, either verbally, non-verbally or using visual expressions. Recently, research in behavioral signal processing has focused on automatically measuring personality traits using different behavioral cues that appear in our daily communication. In this study, we present an approach to automatically recognize personality traits using a video-blog (vlog) corpus, consisting of tran- scription and extracted audio-visual features. We analyzed linguistic, psycholinguistic and emotional features in addi- Tion to the audio-visual features provided with the dataset. We also studied whether we can better predict a trait by identifying other traits. Using our best models we obtained very promising results compared to the official baseline.