TranSentGAT: A Sentiment-Based Lexical Psycholinguistic Graph Attention Network for Personality Prediction

Extended use of social media, leads to personality detection from online content shared by users. While it has numerous applications in different areas such as recommendation systems, most of existing studies focus on superficial, statistical, and explicit user contents, ignoring the knowledge hidden in semantic features. In this study, we proposed a method to explore psycholinguistic knowledge hidden in deep levels of user data for the task of personality prediction. We proposed a method utilizing a fine-tuned domain-specific BERT model to extract features from a sentence and enrich the outputs by leveraging emotional information to highlight the important words. Furthermore, by conducting a double-way-attention mechanism we reflected the information from highlighted words into the whole knowledge extracted from inputs. Then, created a graph by considering extracted embeddings from the last step as node features and developing a dynamic and task-related learning approach to specify edges to connect different pairs of nodes based on a neural network, and leveraged graph attention network to predict personality traits. Finally, experimental results confirmed the effectiveness of our proposed model with 80.63% accuracy, compared to other state-of-the-art studies for the essays dataset. Also, several ablations are conducted to illustrate and verify the impact of different sections and parameters of the proposed architecture.