Machine learning approach to personality type prediction based on the Myers–Briggs Type Indicator ®

Amirhosseini, Mohammad Hossein and Kazemian, Hassan (2020) Machine learning approach to personality type prediction based on the Myers–Briggs Type Indicator ®. Multimodal Technologies and Interaction, 4 (9). pp. 1-15. ISSN 2414-4088

Preview

Text mti-04-00009-v2 (1) published paper final 2.pdf - Published Version Available under License Creative Commons Attribution 4.0. Download (2MB) | Preview

Abstract / Description

Neuro Linguistic Programming (NLP) is a collection of techniques for personality development. Meta programmes, which are habitual ways of inputting, sorting and filtering the information found in the world around us, are a vital factor in NLP. Differences in meta programmes result in significant differences in behaviour from one person to another. Personality types can be recognized through utilizing and analysing meta programmes. There are different methods to predict personality types based on meta programmes. The Myers-Briggs Type Indicator (MBTI) is currently considered as one of the most popular and reliable methods. In this study, a new machine learning method has been developed for personality type prediction based on the MBTI. The performance of the new methodology presented in this study has been compared to other existing methods and the results show better accuracy and reliability. The results of this study can assist NLP practitioners and psychologists in regards to identification of personality types and associated cognitive processes.

Item Type:	Article
Uncontrolled Keywords:	machine learning, personality type prediction, Myers–Briggs Type Indicator®, extreme Gradient Boosting
Subjects:	000 Computer science, information & general works 100 Philosophy & psychology > 150 Psychology
Department:	School of Computing and Digital Media
Depositing User:	Hassan Kazemian
Date Deposited:	08 Apr 2020 08:13
Last Modified:	08 Apr 2020 08:18
URI:	https://repository.londonmet.ac.uk/id/eprint/5720

Downloads

Actions (login required)

View Item