Machine Learning Approach to Personality Type Prediction Based on the Myers–Briggs Type Indicator®

Article


Amirhosseini, M.H. and Kazemian, H. 2020. Machine Learning Approach to Personality Type Prediction Based on the Myers–Briggs Type Indicator®. Multimodal Technologies and Interaction. 4 (Art. 9). https://doi.org/10.3390/mti4010009
AuthorsAmirhosseini, M.H. and Kazemian, H.
Abstract

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.

Keywordsmachine learning; personality type prediction; Myers–Briggs Type Indicator®; extreme Gradient Boosting
JournalMultimodal Technologies and Interaction
Journal citation4 (Art. 9)
ISSN2414-4088
Year2020
PublisherMDPI
Publisher's version
License
File Access Level
Anyone
Digital Object Identifier (DOI)https://doi.org/10.3390/mti4010009
Web address (URL)https://doi.org/10.3390/mti4010009
Publication dates
Online14 Mar 2020
Publication process dates
Accepted12 Mar 2020
Deposited17 Apr 2020
Copyright holder© 2020 The Authors
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