A Comparison of Personality Prediction Classifiers for Personnel Selection in Organizations Based on Industry 4.0

Abstract

Nowadays, the internet has an astonishing amount of useful material for personality mining; nevertheless, many companies fail to exploit the information and screen job candidates using personality tests that fail to grasp the very information they are trying to gather. This research aims to highlight and compare the different machine learning classifiers that can be used to predict the personality of a Spanish-speaking job applicant based on the written content posted on their social networks. The authors conduct experiments considering the most critical measures (such as accuracy, precision, and recall) to evaluate the classification performance. The results show that the random-forest classifier outperforms the other classifiers. It is of utmost importance to correctly assess the resumes to determine the most qualified people in a smart manufacturing position.