TraitBertGCN: Personality Trait Prediction Using BertGCN with Data Fusion Technique

Personality prediction via different techniques is an established and trending topic in psychology. The advancement of machine learning algorithms in multiple fields also attracted the attention of Automatic Personality Prediction (APP). This research proposes a novel TraitBertGCN method with a data fusion technique for predicting personality traits. Initially, this work integrates a pre-trained language model, Bidirectional Encoder Representations from Transformers (BERT), with a three-layer Graph Convolutional Network (GCN) to leverage large-scale language understanding and graph-based learning for personality prediction. This study fuses the two datasets (essays and myPersonality) to overcome the bias and generalize the model across different domains. We fine-tuned our TraitBertGCN model on the fused dataset and then evaluated it on both datasets individually to assess its adaptability and accuracy in varied contexts. We compared the proposed model’s results with previous studies; our model achieved better performance in personality trait prediction across multiple datasets, with an average accuracy of 77.42% on the essays dataset and 87.59% on the myPersonality dataset.