Myers-Briggs Type Indicator analysis using decision-tree-related methods

Research Article
Open access

Myers-Briggs Type Indicator analysis using decision-tree-related methods

Feixiong Chen 1* , Xinyi Shen 2 , Yimin Liu 3 , Yi Wang 4 , Zitao Zhang 5 , Muyin Wang 6
  • 1 Xidian University Xi’an, Shanxi, 710126，China    
  • 2 Foxcroft School, Middleburg, VA, 20117-3727, United States    
  • 3 Grier School, Tyrone, PA, 16686, United States    
  • 4 The University of Melbourne Grattan Street, Parkville,Victoria, 3010, Australia    
  • 5 University of Washington, Seattle WA, 98105, United Stated    
  • 6 Beijing Institute of Technology, Beijing, China    
  • *corresponding author cosmoslight2021@163.com
Published on 22 March 2023 | https://doi.org/10.54254/2755-2721/2/20220564
ACE Vol.2
ISSN (Print): 2755-273X
ISSN (Online): 2755-2721
ISBN (Print): 978-1-915371-19-5
ISBN (Online): 978-1-915371-20-1

Abstract

Myers-Briggs Type Indicator (MBTI) is currently one of the most widely used personality testing tools. MBTI has important value in the field of psychology and career planning. In this essay, a system that can predict people's MBTI features based on their social media posts is proposed. The dataset used in this paper is derived from the online posts of peo-ple with different MBTI personality types. After these posts are preprocessed, they will be analyzed using methods related to decision trees such as random forest and XGBoost. The results from these methods will be compared to other common methods, such as neural networks. The essay measures the ability of the different methods by using the classifying accuracy in the four different dimensions of MBTI. The decision-tree-related methods achieved generally higher accuracy in the task than other types of methods such as neural networks. Methods such as random forest achieved accuracy over 85% on the second la-bel (N/S), and at least 60% accuracy on other labels.

Keywords:

MBTI, personality, decision trees, neural network.

Chen,F.;Shen,X.;Liu,Y.;Wang,Y.;Zhang,Z.;Wang,M. (2023). Myers-Briggs Type Indicator analysis using decision-tree-related methods. Applied and Computational Engineering,2,121-135.
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References

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[2]. Mylonas, C. (2014). Analysis of networking characteristics of different personality types. Internet Archive. http://arxiv.org/abs/1406.3663

[3]. Xu, X., Zeng, W., Ou, P., & Wang, H. (2018, November). Analysis of medical staff on MBTI personality type test. China Medical Herald, 32:160-163.

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[5]. Cui, B.; Qi, C. (2017). Survey Analysis of Machine Learning Methods for Natural Language Processing for MBTI Personality Type Prediction.

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[8]. Hernandez, R., & Scott, I. K. (2017). Predicting Myers-Briggs Type Indicator with text classification. In Proceedings of the 31st Conference on Neural Information Processing Systems. Long Beach, CA.

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[13]. Chen, T., & Guestrin, C. (2016). XGBoost: A scalable tree boosting system. In Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. http://dx.doi.org/10.1145/2939672.2939785

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[15]. Hochreiter, S., & Schmidhuber, J. (1997). Long Short-Term Memory. Neural Computation, 9: 1735-1780. https://doi.org/10.1162/neco.1997.9.8.1735

[16]. Brownlee, J. (2016). Linear Regression for Machine Learning. Machine Learning Mastery. https://machinelearningmastery.com/linear-regression-for-machine-learning/

Cite this article

Chen,F.;Shen,X.;Liu,Y.;Wang,Y.;Zhang,Z.;Wang,M. (2023). Myers-Briggs Type Indicator analysis using decision-tree-related methods. Applied and Computational Engineering,2,121-135.

Data availability

The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.

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About volume

Volume title: Proceedings of the 4th International Conference on Computing and Data Science (CONF-CDS 2022)

ISBN：978-1-915371-19-5(Print) / 978-1-915371-20-1(Online)
Editor：Alan Wang
Conference website: https://www.confcds.org/
Conference date: 16 July 2022
Series: Applied and Computational Engineering
Volume number: Vol.2
ISSN：2755-2721(Print) / 2755-273X(Online)

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References

[1]. Gjurković, M., & Šnajder, J. (2018). Reddit: A Gold Mine for Personality Prediction. In Proceedings of the Second Workshop on Computational Modeling of People's Opinions, Personality, and Emotions in Social Media. New Orleans. pp. 87-97. https://aclanthology.org/ W18-1112

[2]. Mylonas, C. (2014). Analysis of networking characteristics of different personality types. Internet Archive. http://arxiv.org/abs/1406.3663

[3]. Xu, X., Zeng, W., Ou, P., & Wang, H. (2018, November). Analysis of medical staff on MBTI personality type test. China Medical Herald, 32:160-163.

[4]. Golbeck, J., Robles, C., Edmondson, M., & Turner, K. (2011). Predicting personality from twitter. 2011 IEEE Third International Conference on Privacy, Security, Risk and Trust and 2011 IEEE Third International Conference on Social Computing. pp. 149-156. https://doi.org/10.1109/PASSAT/ SocialCom.2011.33

[5]. Cui, B.; Qi, C. (2017). Survey Analysis of Machine Learning Methods for Natural Language Processing for MBTI Personality Type Prediction.

[6]. Eng, Zi Jye. (2020). Personality recognition using composite audio-video features on custom CNN architecture. Final Year Project, UTAR.

[7]. Fung, P., Dey, A., Siddique, F. B., Lin, R., Yang, Y., Wan, Y., & Chan, H. Y. R. (2016). Zara the supergirl: An empathetic personality recognition system. Proceedings of the 2016 Conference of the North American Chapter of theAssociation for Computational Linguistics: Demonstrations. pp. 87-91. https://doi.org/10.18653/v1/N16-3018

[8]. Hernandez, R., & Scott, I. K. (2017). Predicting Myers-Briggs Type Indicator with text classification. In Proceedings of the 31st Conference on Neural Information Processing Systems. Long Beach, CA.

[9]. Keh, S. S., & Cheng, I. (2019). Myers-Briggs Personality Classification and Personality-Specific Language Generation Using Pre-trained Language Models. ArXiv, abs/1907.06333.

[10]. Friedman, J. H. (2002). Stochastic gradient boosting. Computational Statistics & Data Analysis, 38: 367-378. https://doi.org/10.1016/S0167-9473(01)00065-2

[11]. Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., Vanderplas, J., Passos, A., Cournapeau, D., Brucher, M., Perrot, M., & Duchesnay, É. (2011). Scikit-learn: Machine Learning in Python. JMLR 12, 12, 2825-2830. https://scikit-learn.org/stable/about.html#citing-scikit-learn

[12]. Sebastian, A. (2020). A Gentle Introduction To Calculating The TF-IDF Values. Towards Data Science. https://towardsdatascience.com/a-gentle-introduction-to-calculating-the-tf-idf-values-9e391f8a13e5

[13]. Chen, T., & Guestrin, C. (2016). XGBoost: A scalable tree boosting system. In Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. http://dx.doi.org/10.1145/2939672.2939785

[14]. Lecun, Y., Haffner, P., Bottou, L., & Bengio, Y. (1999). Object Recognition with Gradient-Based Learning. In Shape, Contour and Grouping in Computer Vision. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-46805-6_19

[15]. Hochreiter, S., & Schmidhuber, J. (1997). Long Short-Term Memory. Neural Computation, 9: 1735-1780. https://doi.org/10.1162/neco.1997.9.8.1735

[16]. Brownlee, J. (2016). Linear Regression for Machine Learning. Machine Learning Mastery. https://machinelearningmastery.com/linear-regression-for-machine-learning/

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