Psycholinguistic Tripartite Graph Network for Personality Detection

TL;DR

This paper introduces TrigNet, a psycholinguistic tripartite graph network that leverages domain knowledge from LIWC for personality detection, achieving superior accuracy and efficiency over existing models.

Contribution

The paper presents a novel tripartite graph network incorporating psycholinguistic knowledge and a flow GAT to improve personality detection accuracy and computational efficiency.

Findings

TrigNet outperforms state-of-the-art models by 3.47 and 2.10 F1 points.

Flow GAT reduces FLOPS and memory usage by 38% and 32%.

The approach effectively integrates domain knowledge into deep learning for personality analysis.

Abstract

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.