Psycholinguistic Tripartite Graph Network for Personality Detection

• URL: http://arxiv.org/abs/2106.04963v1
• Date: Wed, 9 Jun 2021 10:18:50 GMT
• Title: Psycholinguistic Tripartite Graph Network for Personality Detection
• Authors: Tao Yang, Feifan Yang, Haolan Ouyang, Xiaojun Quan
• Abstract summary: We propose a psycholinguistic knowledge-based tripartite graph network, TrigNet. The graph network injects structural psycholinguistic knowledge from LIWC, a computerized instrument for psycholinguistic analysis. To reduce the computational cost in graph learning, we propose a novel flow graph attention network (GAT) that only transmits messages between neighboring parties.
• Score: 9.620830488893098
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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