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
Authors | Amirhosseini, M.H. and Kazemian, H. |
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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. |
Keywords | machine learning; personality type prediction; Myers–Briggs Type Indicator®; extreme Gradient Boosting |
Journal | Multimodal Technologies and Interaction |
Journal citation | 4 (Art. 9) |
ISSN | 2414-4088 |
Year | 2020 |
Publisher | MDPI |
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 | |
Online | 14 Mar 2020 |
Publication process dates | |
Accepted | 12 Mar 2020 |
Deposited | 17 Apr 2020 |
Copyright holder | © 2020 The Authors |
https://repository.uel.ac.uk/item/87x55
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