Machine Learning-Based Personality Prediction Using the Myers-Briggs Type Indicator

Abstract

In the digital age, artificial intelligence and machine learning are becoming integrated into all aspects of life. The Myers-Briggs Type Indicator (MBTI) is widely utilized in career planning, team building, and counseling. Traditional MBTI tests require active participation and can be time-consuming and subjective. However, using machine learning to predict MBTI types represents a novel research direction. This study explored the application of machine learning to predict an author's MBTI type based on text data, such as social media posts. A successful text-based classifier was developed, establishing a linguistic foundation for understanding MBTI and personality traits. The research utilized an MBTI dataset derived from posts on the Personality Café forum, along with their corresponding types. The dataset underwent rigorous preprocessing to ensure accuracy and consistency. Two models were trained on the processed dataset: random forest and logistic regression. These models effectively manage complex datasets and generate accurate predictions. The study concluded that predicting personality traits from text data using machine learning is feasible. This finding has significant implications for personality theory, paving new pathways for understanding and applying insights from assessments. This research highlights the significant potential of machine learning in enhancing our comprehension of personality traits and establishes a solid foundation for future studies in this field.