Nowadays, a tremendous amount of human communications occur on 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 their relationships. 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, it first tries to acquire a knowing-full representation of the knowledge behind the concepts in the input text by building its equivalent knowledge graph. A knowledge graph collects interlinked descriptions of concepts, entities, and relationships in a machine-readable form. Practically, it provides a machine-readable cognitive understanding of concepts and semantic relationships among them. Then, applying the attention mechanism, it attempts to pay attention to the most relevant parts of the graph to predict the personality traits of the input text. We used 2,467 essays from the Essays Dataset. The results demonstrated that KGrAt-Net considerably improved personality prediction accuracies (up to 70.26% on average). Furthermore, KGrAt-Net also uses knowledge graph embedding to enrich the classification, which makes it even more accurate (on average, 72.41%) in APP.
翻译:目前,在互联网通信基础设施(如社交网络、电子邮件、论坛、组织通信平台等)上出现了大量的人际通信。事实上,通过书面或交换文本自动预测或评估个人个性将有助于改善他们的关系。为此,本文件旨在提议KGrAt-Net,这是一个知识图表关注网络文本分类器。它首次根据五大个性特征,将知识图关注网络用于执行自动个性预测(APP)。在开展一些预处理活动之后,它首先试图通过建立其等同的知识图表,获得输入文本中概念背后知识的知情充分体现。知识图收集了概念、实体和关系以机器可读的形式相互关联的描述。实际上,它提供了一种机器可读的概念认知理解和它们之间的语义关系。随后,它运用关注机制,试图关注图表中最相关的部分,以预测输入文本的个性特征。我们使用了2,467篇来自Esssaid dataset的论文, 数据图集收集了相关概念、实体和关系,以机器可读的形式收集。实际上,KGrAt-commal利用了70的高级预测结果。