Text-Based Automatic Personality Prediction Using KGrAt-Net; A Knowledge Graph Attention Network Classifier

• URL: http://arxiv.org/abs/2205.13780v1
• Date: Fri, 27 May 2022 06:33:09 GMT
• Title: Text-Based Automatic Personality Prediction Using KGrAt-Net; A Knowledge Graph Attention Network Classifier
• Authors: Majid Ramezani and Mohammad-Reza Feizi-Derakhshi and Mohammad-Ali Balafar
• Abstract summary: KGrAt-Net is a knowledge graph attention network to perform Automatic Personality Prediction (APP) The results demonstrated that KGrAt-Net considerably improved the personality prediction accuracies.
• Score: 8.357801312689622
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Nowadays, a tremendous amount of human communications take place on the Internet-based communication infrastructures, like social networks, email, forums, organizational communication platforms, etc. Indeed, the automatic prediction or assessment of individuals' personalities through their written or exchanged text would be advantageous to ameliorate the relationships among them. To this end, this paper aims to propose KGrAt-Net which is a Knowledge Graph Attention Network text classifier. For the first time, it applies the knowledge graph attention network to perform Automatic Personality Prediction (APP), according to the Big Five personality traits. After performing some preprocessing activities, first, it tries to acquire a knowingful representation of the knowledge behind the concepts in the input text through building its equivalent knowledge graph. A knowledge graph is a graph-based data model that formally represents the semantics of the existing concepts in the input text and models the knowledge behind them. Then, applying the attention mechanism, it efforts to pay attention to the most relevant parts of the graph to predict the personality traits of the input text. The results demonstrated that KGrAt-Net considerably improved the personality prediction accuracies. Furthermore, KGrAt-Net also uses the knowledge graphs' embeddings to enrich the classification, which makes it even more accurate in APP.

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