Enhancing Personality Type Prediction with Ensemble Models: A Robust Predictive Approach

This research proposes an inventive methodology for deducing individuals’ personality types by amalgamating information from diverse social media platforms with scores from the Myers-Briggs Type Indicator (MBTI). This involves creating a unique dataset from text data gathered from various social media sources, utilizing Advanced Natural Language Processing (NLP) to classify participants into 16 MBTI personality categories. The primary objective is establishing a robust data preparation process, ensuring data accuracy, and developing and rigorously evaluating a prediction model using machine learning techniques like logistic regression, support vector machines, and neural networks. Accuracy is used as the performance evaluation metric compared with other machine learning algorithms. The study’s impact is significant, extending beyond personality inference to offer insights into understanding individuals’ online behaviour and personalities. This has implications for user experience research, social media marketing, and mental health studies, benefiting from the insights and methodologies generated. By leveraging social media data and machine learning, this research represents a pioneering approach to personality analysis, unlocking novel avenues for comprehending human behaviour and cognition.