PerKG: A Personality Knowledge Graph for Personality Analysis

Yangfu Zhu, Zhanming Guan, Siqi Wei, Bin Wu

Abstract: With the blossoming of online social networks (OSN), personality analysis based on OSN texts has gained much research attention in recent years. The previous methods mainly focus on human-designed features extracted through psychological dictionaries or semantic features extracted through language models. However, the shallow statistics features can not fully convey the personality information and the language models can not capture enough psychological background knowledge. Besides, the lack of large labeled datasets has been a serious obstacle impending further research. To tackle these problems, we propose a personality analysis model, namely PerKG, which combines personality knowledge graph and heterogeneous graph representation learning to exploit external knowledge from psycholinguistics and learn the group-level information to predict users’ personalities accurately. Specifically, we construct a personality knowledge graph based on existing psycholinguistics knowledge. And then, for each user, we align the user information with the knowledge graph to obtain the personality heterogeneous graph. Finally, the personality vector of each entity node is learned for prediction by designing a walk strategy on the personality heterogeneous graph. Detailed experimentation shows that our proposed PerKG architecture can effectively improve the performance and alleviate the label sparsity problem of personality analysis.