An Efficient Deep Learning-Based Hybrid Framework for Personality Trait Prediction through Behavioral Analysis

Social media outlets deliver customers a medium for communication, exchange, and expression of their thoughts with others. The advent of social networks and the fast escalation of the quantity of data have created opportunities for textual evaluation. Utilising the user corpus, characteristics of social platform users, and other data, academic research may accurately discern the personality traits of users. This research examines the traits of consumer personalities. Usually, personality tests administered by psychological experts via interviews or self-report questionnaires are costly, time-consuming, complex, and labour-intensive. Currently, academics in computational linguistics are increasingly focused on predicting personality traits from social media data. An individual’s personality comprises their traits and behavioral habits. To address this distinction, we propose a novel LSTM approach (BERT-LIWC-LSTM) that simultaneously incorporates users’ enduring and immediate personality characteristics for textual personality recognition. Long-term Personality Encoding in the proposed paradigm captures and represents persisting personality traits. Short-term Personality Capturing records changing personality states. Experimental results demonstrate that the designed BERT-LIWC-LSTM model achieves an average improvement in accuracy of 3.41% on the Big Five dataset compared to current methods, thereby justifying the efficacy of encoding both stable and dynamic personality traits simultaneously through long- and short-term feature interaction.