Psycholinguistic Tripartite Graph Network for Personality Detection (2021)

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 use 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 in LIWC, a computerized instrument for psycholinguistic analysis, by constructing a heterogeneous tripartite graph. The 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.

## introduction personality detection from online posts aims to identify one's personality traits from the online texts he creates. this emerging task has attracted great interest from researchers in computational psycholinguistics and natural language processing due to the extensive application scenarios such as personalized recommendation systems @xcite , job screening @xcite and psychological studies @xcite . psychological research shows that the words people use in daily life reflect their cognition, emotion, and personality @xcite . as a major psycholinguistic instrument, linguistic inquiry and word count (liwc) @xcite divides words into psychologically relevant categories (e.g., function, affect, and social as shown in figure 1 ) and is commonly used to extract psycholinguistic features in conventional methods @xcite . nevertheless, most recent works @xcite @xcite @xcite tend to adopt deep neural networks (dnns) to represent the posts and build predictive models in a data-driven manner. they first encode each post separately and then aggregate the post representations into a user representation. although numerous improvements have been made over the traditional methods, they are likely to suffer from limitations as follows. first, the input of this task is usually a set of topic-agnostic posts, some of which may contain few personality cues. hence, directly aggregating these posts based on their contextual representations may inevitably introduce noise. second, personality detection is a typical data-hungry task since it is non-trivial to obtain personality tags, while dnns implicitly extract personality cues from the texts and call for tremendous training data. naturally, it is desirable to explicitly introduce psycholinguistic knowledge into the models to capture critical personality cues. motivated by the above discussions, we propose a psycholinguistic knowledge-based tripartite graph network, namely trignet, which consists of a tripartite graph network to model the psycholinguistic knowledge and a graph initializer using a pre-trained language model such as bert @xcite to generate the initial representations for all the nodes. as illustrated in figure 1 , a specific tripartite graph is constructed for each user, where three heterogeneous types of nodes, namely post, word, and category, are used to represent the posts of a user, the words contained both in his posts and the liwc dictionary, and the psychologically relevant categories of the words, respectively. the edges are determined by the subordination between word and post nodes as well as between word and category nodes. besides, considering that there are no direct edges between homogeneous nodes (e.g., between post nodes) in the tripartite graph, a novel flow gat is proposed to only transmit messages between neighboring parties to reduce the computational cost and to allow for more effective interaction between nodes. finally, we regard the averaged post node representation as the final user representation for personality classification. benefiting from the tripartite graph structure, the interaction between posts is based on psychologically relevant words and categories rather than topic-agnostic context. we conduct extensive experiments on the kaggle and pandora datasets to evaluate our trignet model. experimental results show that it achieves consistent improvements over several strong baselines. comparing to the state-of-the-art model, sn+att @xcite , trignet brings a remarkable boost of 3.47 in averaged macro-f1 (%) on kaggle and a boost of 2.10 on pandora. besides, thorough ablation studies and analyses are conducted and demonstrate that the tripartite graph and the flow gat play an irreplaceable role in the boosts of performance and decreases of computational cost. our contributions are summarized as follows: ## our approach personality detection can be formulated as a multidocument multi-label classification task @xcite . formally, each user has a set @xmath0, p 2 , . . . , p r } of posts. let p @xmath1,1 , w i,2 , . . . , w i,s ] be the i-th post with s words, where p i can be viewed as a document. the goal of this task is to predict t personality traits @xmath2, where y @xmath3, 1} is a binary variable. figure 2 presents the overall architecture of the proposed trignet, which consists of a tripartite graph network and a bert-based graph initializer. the former module aims to explicitly infuse psycholinguistic knowledge to uncover personality cues contained in the posts and the latter to encode each post and provide initial embeddings for the tripartite graph nodes. in the following subsections, we detail how the two modules work in four steps: graph construction, graph initialization, graph learning, and merge & classification. ## experiments in this section, we introduce the datasets, baselines, and settings of our experiments. ## results and analyses in this section, we report the overall results and provide thorough analyses and discussions. ## conclusion in this work, we proposed a novel psycholinguistic knowledge-based tripartite graph network, trignet, for personality detection. trignet aims to introduce structural psycholinguistic knowledge from liwc via constructing a tripartite graph, in which interactions between posts are captured through psychologically relevant words and categories rather than simple document-word or sentence-word relations. besides, a novel flow gat that only transmits messages between neighboring parties was developed to reduce the computational cost. extensive experiments and analyses on two datasets demonstrate the effectiveness and efficiency of trignet. this work is the first effort to leverage a tripartite graph to explicitly incorporate psycholinguistic knowledge for personality detection, providing a new perspective for exploiting domain knowledge. liang yao, chengsheng mao, and yuan luo. 2019. graph convolutional networks for text classification. in proceedings of the aaai conference on artificial intelligence, volume 33, pages 7370-7377. le zhang, songyou peng, and stefan winkler. 2019. persemon: a deep network for joint analysis of apparent personality, emotion and their relationship.