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

Abstract

Extended use of social media, lead 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 users data for the task of personality prediction. We proposed a method utilizing fine-tuned domain-specific BERT model to extract features from a sentence, and enriched 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 last step as node features and developing a dynamic and task-realted 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% of accuracy, compared to other stateof-the-art studies for the essays dataset. Also, several ablations are conducted to illustrate and verify the impact of different sections and parameteres of the proposed architecture.