Multimodal Bigfive Personality Trait Analysis Using Communication Skill Indices And Multiple Discussion Types Dataset

This paper focuses on multimodal analysis in multiple discussion types dataset for estimating BigFive personality traits. The analysis was conducted to achieve two goals: First, clarifying the effectiveness of multimodal features and communication skill indices to predict the BigFive personality traits. Second, identifying the relationship among multimodal features, discussion type, and the BigFive personality traits. The MATRICS corpus, which contains of three discussion task types dataset, was utilized in this experiment. From this corpus, three sets of multimodal features (acoustic, head motion, and linguistic) and communication skill indices were extracted as the input for our binary classification system. The evaluation was conducted by using F1-score in 10-fold cross validation. The experimental results showed that the communication skill indices are important in estimating agreeableness trait. In addition, the scope and freedom of conversation affected the performance of personality traits estimator. The freer a discussion is, the better personality traits estimator can be obtained.