Contrastive Graph Transformer Network for Personality Detection

Yangfu Zhu, Linmei Hu*, Xinkai Ge, Wanrong Peng, Bin Wu*

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

10 Citations (Scopus)

Abstract

Personality detection is to identify the personality traits underlying social media posts. Most of the existing work is mainly devoted to learning the representations of posts based on labeled data. Yet the ground-truth personality traits are collected through time-consuming questionnaires. Thus, one of the biggest limitations lies in the lack of training data for this data-hungry task. In addition, the correlations among traits should be considered since they are important psychological cues that could help collectively identify the traits. In this paper, we construct a fully-connected post graph for each user and develop a novel Contrastive Graph Transformer Network model (CGTN) which distills potential labels of the graphs based on both labeled and unlabeled data. Specifically, our model first explores a self-supervised Graph Neural Network (GNN) to learn the post embeddings. We design two types of post graph augmentations to incorporate different priors based on psycholinguistic knowledge of Linguistic Inquiry and Word Count (LIWC) and post semantics. Then, upon the post embeddings of the graph, a Transformer-based decoder equipped with post-to-trait attention is exploited to generate traits sequentially. Experiments on two standard datasets demonstrate that our CGTN outperforms the state-of-the-art methods for personality detection.

Original languageEnglish
Title of host publicationProceedings of the 31st International Joint Conference on Artificial Intelligence, IJCAI 2022
EditorsLuc De Raedt, Luc De Raedt
PublisherInternational Joint Conferences on Artificial Intelligence
Pages4559-4565
Number of pages7
ISBN (Electronic)9781956792003
Publication statusPublished - 2022
Externally publishedYes
Event31st International Joint Conference on Artificial Intelligence, IJCAI 2022 - Vienna, Austria
Duration: 23 Jul 202229 Jul 2022

Publication series

NameIJCAI International Joint Conference on Artificial Intelligence
ISSN (Print)1045-0823

Conference

Conference31st International Joint Conference on Artificial Intelligence, IJCAI 2022
Country/TerritoryAustria
CityVienna
Period23/07/2229/07/22

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