An enhanced personality detection system through user’s digital footprints

Abstract

Abstract One of the most important aspects of any person's life is personality, which affects one's speech, decision, well-being, feeling and mental health. Personality detection is usually based on data collected by a questionnaire that comprises some critical problems such as the lack of direct access to the individuals and explicit personal information. However nowadays, one of the valuable resources for such studies is social networks. The footprint and tracking of users on social networks have provided valuable information for personality recognition. Specifically, this research introduces an intelligence personality recognition system based on modeling user behavior using sophisticated features, i.e., Statistical, Emotional, and Linguistic. Furthermore, a dataset called KNTU_Personality based on the MBTI personality model with the profile information and tweets has been collected. The experimental study follows two scenarios with complementing objectives. First the sensitivity analysis is performed respecting to setting parameters, introduced features and different learning algorithms. Next the proposed system has been compared with well-known personality detection systems. The results demonstrate the superiorities of the proposed system regarding its counterparts in terms of F-Score, Precision, Recall and Accuracy.