Occupational Trait Models: Meta-Analysis of Personality Prediction

Abstract

In the modern business environment, a candidate's personality is equally as important as their skill set. Success in both one’s personal and professional life depends on one's personality. Therefore, the recruiter needs to be aware of the applicant's personality traits. The traditional approach of hiring individuals is manually shortlisting applicant resumes following the needs of the business. Examining the candidate's curriculum vitae or doing a standard review are two common techniques to determine a person's personality. The two initial criteria would be the results of the aptitude/personality test and a CV review.

This project suggests a system that streamlines the process of categorizing applicants based on qualifications and personality assessments during the hiring process. We have set up a system that treats the candidate's skill set and personality test equally and the same. Character assessment and expectations are extremely well-known in today's society. In some industries in the departments such as recruitment and medical counselling, the current system is not that much helpful for extracting the user's personality. With the help of Natural Language Processing (NLP) methods, this study examines a variety of machine learning methods for accurately predicting personality from CV analysis and personality tests. Results demonstrate that the logistic regression algorithm performed better other than algorithms including KNN, Logistic Regression, SVM, and Naive Bayes in terms of accuracy.