Psycholinguistic Tripartite Graph Network for Personality Detection
This work addresses the problem of enhancing personality detection accuracy and efficiency for researchers and practitioners in computational linguistics and psychology, representing an incremental improvement with novel graph-based integration of domain knowledge.
The paper tackles personality detection from online posts by proposing TrigNet, a psycholinguistic knowledge-based tripartite graph network that incorporates LIWC data, resulting in improved F1 scores by 3.47 and 2.10 points over state-of-the-art models and reducing computational costs by 38% in FLOPS and 32% in memory.
Most of the recent work on personality detection from online posts adopts multifarious deep neural networks to represent the posts and builds predictive models in a data-driven manner, without the exploitation of psycholinguistic knowledge that may unveil the connections between one's language usage and his psychological traits. In this paper, we propose a psycholinguistic knowledge-based tripartite graph network, TrigNet, which consists of a tripartite graph network and a BERT-based graph initializer. The graph network injects structural psycholinguistic knowledge from LIWC, a computerized instrument for psycholinguistic analysis, by constructing a heterogeneous tripartite graph. The graph initializer is employed to provide initial embeddings for the graph nodes. To reduce the computational cost in graph learning, we further propose a novel flow graph attention network (GAT) that only transmits messages between neighboring parties in the tripartite graph. Benefiting from the tripartite graph, TrigNet can aggregate post information from a psychological perspective, which is a novel way of exploiting domain knowledge. Extensive experiments on two datasets show that TrigNet outperforms the existing state-of-art model by 3.47 and 2.10 points in average F1. Moreover, the flow GAT reduces the FLOPS and Memory measures by 38% and 32%, respectively, in comparison to the original GAT in our setting.