Multimodal Sentiment Analysis for Personality Prediction

Personality is a set of traits and unique characteristics that give both consistency and individuality to a person’s behavior. As personality is accepted as an indicator of job performance, recruiters aim to retrieve these behavior traits in the screening process. One issue is that using personality questionnaires is less favored by applicants and negatively affects the pace of the recruitment process. Therefore, it is desired to infer one’s personality traits during an interview. Nowadays, companies tend to use Asynchronous Video Interviews (AVIs), an interview setting where applicants record their answers to pre-defined questions and an interviewer is not present. These recordings are valuable to recruiters since emotions expressed in the AVIs reveal personality insights. With the increasing interest in understanding human behavior, Multimodal Sentiment Analysis (MSA) has emerged as a popular research field. MSA aims to recognize the expressed emotions and sentiment in multimodal data. In this paper, we investigate how MSA can assist personality prediction in AVIs. We propose a novel emotion-to-personality prediction method where dimensions of the Big Five model of personality are predicted based on the emotion distribution in an interview. We collect an AVI dataset in order to test our method. The use of self-assessment i.e. personality questionnaire shows a unique and novel way of validating the personality prediction model on interviews from the AVI dataset. Our findings show that the emotion-to-personality prediction method achieves promising results when predicting the three strongest personality traits, based on the Big Five personality traits, exhibited by an individual in the AVI.