Personality recognition using convolutional neural networks

Abstract

Personality Recognition is an emerging task in Natural Language Processing due to its potential applications. However, the models which address this task rely on handcrafted resources; therefore, they are restricted by the domain of the problem and by the availability of resources. We propose a Convolutional Neural Network architecture trained using pre-trained word embeddings that is capable of learning the best features for the task at hand without any external dependence. The results show the potential of this approximation. The proposed model achieves comparable results with state-of-the-art models and is able to predict the personality traits of authors regardless of the social network and the availability of resources.