A Systematic Literature Review of Personality Trait Classification from Textual Content

The day-to-day use of digital devices with Internet access, such as tablets and smartphones, has increased exponentially in recent years and this has had a consequent effect on the usage of the Internet and social media networks. When using social networks, people share personal data that is broadcast between users, which provides useful information for organizations. This means that characterizing users through their social media activity is an emerging research area in the field of Natural Language Processing (NLP) and this paper will present a review of how personality can be detected using online content. Approach A systematic literature review identified 30 papers published between 2007 and 2019, while particular inclusion and exclusion criteria were used to select the most relevant articles. Outcomes This review describes a variety of challenges and trends, as well as providing ideas for the direction of future research. In addition, personality trait identification and techniques were classified into different types, including deep learning, machine learning (ML) and semi-supervised/hybrid. Implications This paper's outcomes will not only facilitate insight into the various personality types and models but will also provide knowledge about the relevant detection techniques. Novelty While prior studies have conducted literature reviews in the personality trait detection field, the systematic literature review in this paper provides specific answers to the proposed research questions. This is novel to this field as this particular type of study has not been conducted before.