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
Permalink -

https://repository.uel.ac.uk/item/87x55

Download files


Publisher's version
mti-04-00009-v2.pdf
License: CC BY 4.0
File access level: Anyone

  • 403
    total views
  • 409
    total downloads
  • 10
    views this month
  • 6
    downloads this month

Export as

Related outputs

An AI Powered System to Detect Autism Spectrum Disorder in Toddlers
Amirhosseini, M.H., Alam, N, Kalabi, F. and Virdee, B. 2024. An AI Powered System to Detect Autism Spectrum Disorder in Toddlers . ICDAM 2024: 5th International Conference on Data Analytics & Management. London, UK 14 - 15 Jun 2024 Springer.
Predictive precision in battery recycling: unveiling lithium battery recycling potential through machine learning
Valizadeh, A., Amirhosseini, M. H. and Ghorbani, Y. 2024. Predictive precision in battery recycling: unveiling lithium battery recycling potential through machine learning. Computers and Chemical Engineering. 183 (Art. 108623). https://doi.org/10.1016/j.compchemeng.2024.108623
An artificial intelligence approach to predicting personality types in dogs
Amirhosseini, M. H., Yadav, V., Serpell, J. A., Pettigrew, P. and Kain, P. 2024. An artificial intelligence approach to predicting personality types in dogs. Scientific Reports. 14 (Art. 2404). https://doi.org/10.1038/s41598-024-52920-9
Forecasting Bitcoin Prices in the Context of the COVID-19 Pandemic Using Machine Learning Approaches
Sontakke, P., Jafari, F., Saeedi, M. and Amirhosseini, M. 2024. Forecasting Bitcoin Prices in the Context of the COVID-19 Pandemic Using Machine Learning Approaches. 4th International Conference on Data Analytics & Management (ICDAM-2023). London, UK 23 - 24 Jun 2023 Springer. https://doi.org/10.1007/978-981-99-6544-1_7
An AI powered system to enhance self-reflection practice in coaching
Jelodari, M., Amirhosseini, M. H. and Giraldez Hayes, A. 2023. An AI powered system to enhance self-reflection practice in coaching. Cognitive Computation and Systems. 5 (4), pp. 243-254. https://doi.org/10.1049/ccs2.12087
Sentiment-Driven Cryptocurrency Price Prediction: A Machine Learning Approach Utilizing Historical Data and Social Media Sentiment Analysis
Bhatt, S., Ghazanfar, M. and Amirhosseini, M. 2023. Sentiment-Driven Cryptocurrency Price Prediction: A Machine Learning Approach Utilizing Historical Data and Social Media Sentiment Analysis. Machine Learning and Applications: An International Journal (MLAIJ). 10 (2/3), pp. 1-15. https://doi.org/10.5121/mlaij.2023.10301
Machine Learning based Cryptocurrency Price Prediction using historical data and Social Media Sentiment
Bhatt, S., Ghazanfar, M. and Amirhosseini, M. 2023. Machine Learning based Cryptocurrency Price Prediction using historical data and Social Media Sentiment . 5th International Conference on Machine Learning & Applications (CMLA 2023). Sydney, Australia 17 - 18 Jun 2023 AIRCC Publishing Corporation.
A Machine Learning Approach to Identify the Preferred Representational System of a Person
Amirhosseini, M. and Wall, J. 2022. A Machine Learning Approach to Identify the Preferred Representational System of a Person. Multimodal Technologies and Interaction. 6 (12), p. 112. https://doi.org/10.3390/mti6120112
Application of Graph-Based Technique to Identity Resolution
Kazemian, H., Amirhosseini, M. H. and Phillips, M. 2022. Application of Graph-Based Technique to Identity Resolution. AIAI 2022: 18th International Conference on Artificial Intelligence Applications and Innovations. Crete, Greece 17 - 20 Jun 2022 Springer. https://doi.org/10.1007/978-3-031-08333-4_38
A Rule and Graph-Based Approach for Targeted Identity Resolution on Policing Data
Phillips, M., Amirhosseini, M. and Kazemian, H. 2020. A Rule and Graph-Based Approach for Targeted Identity Resolution on Policing Data. 2020 IEEE Symposium Series on Computational Intelligence. Online 01 - 04 Dec 2020 IEEE. https://doi.org/10.1109/SSCI47803.2020.9308182
Automating the process of identifying the preferred representational system in Neuro Linguistic Programming using Natural Language Processing
Amirhosseini, M.H. and Kazemian, H. 2019. Automating the process of identifying the preferred representational system in Neuro Linguistic Programming using Natural Language Processing. Cognitive Processing. 20 (2), p. 175–193. https://doi.org/10.1007/s10339-019-00912-3
Natural Language Processing approach to NLP Meta model automation
Amirhosseini, M.H., Kazemian, H., Ouazzane, K. and Chandler, C. 2018. Natural Language Processing approach to NLP Meta model automation. 2018 International Joint Conference on Neural Networks (IJCNN). Rio de Janeiro, Brazil 08 - 13 Jul 2018 IEEE. https://doi.org/10.1109/IJCNN.2018.8489609