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IJML 2024 Vol.14(1): 24-29
DOI: 10.18178/ijml.2024.14.1.1153

MBTI Personality Classification through Analyzing CVs/ Personal Statements for e-Recruitment

Himaya Perera* and Lakshan Costa
Himaya Perera and Lakshan Costa are with the Informatics Institute of Technology, Sri Lanka.
Email: himaya.2019379@iit.ac.lk.org (H.P.); lakshan.c@iit.ac.lk(L.C.)
*Corresponding author

Manuscript received May 10, 2023; revised June 14, 2023; accepted July 2, 2023; published March 15, 2024

Abstract—With the advent of the internet and globalization, most services have become automated, including the recruitment process. E-recruitment systems have gained popularity due to their ability to automate various steps of recruitment. However, while some systems filter and screen CVs for skills, they do not assess a candidate's personality, which is crucial in determining their compatibility with a company's culture and practices. This compatibility leads to effective recruitment and happier, more productive employees who stay at the company longer. Due to the lack of a personality detection mechanism, recruiters spend a significant amount of time conducting multiple interviews to assess candidate fit. To address this issue, the authors propose a system that uses fine-tuned BERT to detect a candidate's MBTI personality from their CV or personal statement. The proposed system achieved an average accuracy of 72.14% per class and an AUC range of 0.85-0.90, following pre-processing of the Kaggle MBTI personality types dataset to address class imbalance.

Keywords—MBTI, personality, multi-class classification


Cite: Himaya Perera and Lakshan Costa, "MBTI Personality Classification through Analyzing CVs/ Personal Statements for e-Recruitment," International Journal of Machine Learning vol. 14, no. 1, pp. 24-29, 2024.

Copyright © 2024 by the authors. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).

General Information

  • E-ISSN: 2972-368X
  • Abbreviated Title: Int. J. Mach. Learn.
  • Frequency: Quaterly
  • DOI: 10.18178/IJML
  • Editor-in-Chief: Dr. Lin Huang
  • Executive Editor:  Ms. Cherry L. Chen
  • Abstracing/Indexing: Inspec (IET), Google Scholar, Crossref, ProQuest, Electronic Journals LibraryCNKI.
  • E-mail: ijml@ejournal.net

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