  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.
