Improving Intelligent Personality Prediction using Myers-Briggs Type Indicator and Random Forest Classifier

The term “personality” can be defined as the mixture of features and qualities that built an individual's distinctive characters, including thinking, feeling and behaviour. Nowadays, it is hard to select the right employees due to the vast pool of candidates. Traditionally, a company will arrange interview sessions with prospective candidates to know their personalities. However, this procedure sometimes demands extra time because the total number of interviewers is lesser than the total number of job seekers. Since technology has evolved rapidly, personality computing has become a popular research field that provides personalisation to users. Currently, researchers have utilised social media data for auto-predicting personality. However, it is complex to mine the social media data as they are noisy, come in various formats and lengths. This paper proposes a machine learning technique using Random Forest classifier to automatically predict people's personality based on Myers–Briggs Type Indicator® (MBTI). Researchers compared the performance of the proposed method in this study with other popular machine learning algorithms. Experimental evaluation demonstrates that Random Forest classifier performs better than the different three machine learning algorithms in terms of accuracy, thus capable in assisting employers in identifying personality types for selecting suitable candidates.

@article{Abidin2020,
  title     = {Improving Intelligent Personality Prediction using Myers-Briggs Type Indicator and Random Forest Classifier},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {11},
  number    = {11},
  year      = {2020},
  publisher = {The Science and Information Organization},
  author    = {Nur Haziqah Zainal Abidin and Muhammad Akmal Remli and Noorlin Mohd Ali and Danakorn Nincarean Eh Phon and Nooraini Yusoff and Hasyiya Karimah Adli and Abdelsalam H Busalim},
  doi       = {10.14569/IJACSA.2020.0111125},
  url       = {https://doi.org/10.14569/IJACSA.2020.0111125}
}