Evaluating Machine Learning Algorithms for Automated Personality Judgment

Abstract

Traditional methods of personality judgement, such as self-report questionnaires and manual assessments, are often limited by subjectivity, time consumption and vulnerability to social desirability bias. These drawbacks highlight the need for automated and data-driven techniques that can provide more objective and scalable personality evaluation. In this study, we explore the use of machine learning (ML) algorithms to predict personality traits and systematically compare their performances. Models including linear regression, decision tree, random forest, support vector machine (SVM) and AdaBoost are implemented on a benchmark dataset. The algorithms are evaluated using standard metrics such as accuracy, precision, recall and F1-score to ensure a comprehensive analysis. Results reveal distinct strengths and weaknesses across classifiers, offering insights into the most effective approaches for personality judgement. The findings demonstrate the potential of ML in advancing personality assessment and provide a foundation for building reliable, interpretable and scalable solutions. Such approaches can be applied in domains like human resource management, education and mental health, where accurate personality insights are essential for informed decision-making and personalised interventions.