A Dimensional Representation of Depressive Text

Abstract

Depression is presently one of society's main psychological disorders. An intensified public mental health concern has been prompted by recent experiences with the emergence of corona virus disease 2019 (COVID-19). At present, the emphasis of research on human emotional state representation has changed from basic emotions to a large number of emotions in continuous three-dimensional space owing to the complexity of describing and evaluating a vast number of emotions within a single framework. Significant considerations of 3D continuous valence, arousal and dominance space while overseeing mental health issues are important as they relate to the expression of emotion and behavioural reactions. The goal of this research is to design a machine learning regressor modal to estimate the continuous valence, arousal and dominance score which results from the process of emotional intelligence via text interpretation. In the pursuit of goal, EmoBank dataset, which contains text information as well as valence–arousal–dominance values and for validation ISEAR, a labelled corpus of categorical emotions datasets is used. We learn an embedding using three pre-trained word embeddings: word2vec, Doc2vec and BERT, and find that BERT significantly outperforms the result. In a future study, the regressor model will be adopted in depression detection by distributing the categorical negative emotions in terms of VAD.