  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.
