Personality refer to the distinctive set of characteristics of a person that effect their habits, behaviour's, attitude and pattern of thoughts. Text available on Social Networking sites provide an opportunity to recognize individual's personality traits automatically. In this proposed work, Machine Learning Technique, XGBoost classifier is used to predict four personality traits based on Myers-Briggs Type Indicator (MBTI) model, namely Introversion-Extroversion(I-E), iNtuition-Sensing(N-S), Feeling-Thinking(F-T) and Judging-Perceiving(J-P) from input text. Publically available benchmark dataset from Kaggle is used in experiments. The skewness of the dataset is the main issue associated with the prior work, which is minimized by applying Re-sampling technique namely random over-sampling, resulting in better performance. For more exploration of the personality from text, pre-processing techniques including tokenization, word stemming, stop words elimination and feature selection using TF IDF are also exploited. This work provides the basis for developing a personality identification system which could assist organization for recruiting and selecting appropriate personnel and to improve their business by knowing the personality and preferences of their customers. The results obtained by all classifiers across all personality traits is good enough, however, the performance of XGBoost classifier is outstanding by achieving more than 99% precision and accuracy for different traits.

AbstractThe 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.