A hierarchical transformer network with label attention for personality prediction by MBTI classification

Abstract

Personality prediction is one of the emerging researches with applications in multiple fields such as psychology, ai, recommendation system, job screening, education, police department (to monitor and enforce law and order). With the evolution of deep learning models, personality prediction can be formulated as a classification problem from social media texts. More recently, transformer-based models have demonstrated impressive results in personality prediction of individuals. This paper proposes a hierarchical transformer enabled with label attention mechanism for multiclass and binary classification of personality traits from the Myers-Briggs Type Indicator (MBTI) dataset, named as Hierarchical Transformer network with Label Attention for Personality Prediction (HT-LA-PP). By capturing relationships between words in sentences using a hierarchical transformer enabled with self-attention, followed by label attention to each personality trait, the proposed model surpasses the performance of similar deep learning and Transformer-based models with significant accuracy and robustness.