Advanced prediction of Myers-Briggs personality traits using hybrid CNN-LSTM models and textual data

Abstract

This study investigates the application of deep learning models to predict MBTI personality types, leveraging their potential to identify prevalent patterns and support organizational interventions. Four models were evaluated: 1-layer, 2-layer, and 3-layer Convolutional Neural Networks (CNNs), and a hybrid CNN-LSTM model. The performance of these models was assessed using metrics such as accuracy, precision, recall, F1-score, and confusion matrices. Training involved 60 epochs with regularization techniques like early stopping, batch normalization, and dropout to mitigate overfitting and enhance model performance. Key findings reveal that the 1-layer CNN demonstrated superior performance among standalone CNN models, while the hybrid CNN-LSTM outperformed all models, achieving a high F1-score of 97.16%. The hybrid model's balanced metrics highlight its robustness and efficiency in classifying personality types within the MBTI dataset. Insights from confusion matrices further emphasize the hybrid model's ability to provide correct predictions across multiple classes, surpassing simpler and multi-layer architectures. The study underscores the utility of hybrid architectures in complex classification tasks and their potential for workplace applications, such as task assignments and promoting inclusivity. By integrating personality insights, organizations can foster more effective work environments, boosting employee satisfaction and productivity. These findings offer a solid foundation for refining model selection in future personality prediction research.