What You Write Represents Your Personality: A Dual Knowledge Stream Graph Attention Network for Personality Detection

The goal of the personality detection task is to determine a person’s personality traits using their social media posts. Recently, researchers have turned away from a fully data-driven approach and begun employing prior knowledge about psycholinguistic to guide their research. People typically post on social media to express their opinions or share their emotions. Therefore, it is crucial to uncover the traits and disparities in how individuals with different personalities express themselves. However, current research based on psycholinguistic principles only examines these differences superficially, failing to conduct more granular analyses, such as exploring emotions. In this paper, we propose an innovative approach that blends psycholinguistic and prior emotional knowledge to acquire features at varying levels. Our model, named Dual Knowledge Stream Graph Attention Network (DKSGAT), comprises of two streams. One stream represents posts at the psycholinguistic level, while the other encodes words at a more finely-grained emotional level based on prior emotional knowledge. Both streams’ representations are then obtained to make joint inferences about personality traits. Our approach outperforms previous studies in predicting the Big Five personality and MBTI personality, as demonstrated through testing on two different public datasets.