A deep learning approach for the depression detection of social media data with hybrid feature selection and attention mechanism

Abstract

Depression is a severe mental health issue. The user-generated content on social media (SM) is growing nowadays. Some computational approaches have been proposed for detecting depression based on users' SM data. However, because of the use of formal language, short range of words and misspellings in the SM data, depression detection (DD) is a challenging task. This paper proposes a novel deep learning (DL) technique for performing DD of the SM data with the help of the hybrid feature selection (FS) mechanism. Initially, two publicly available datasets containing user tweets are collected for implementing the proposed research model. Then the collected datasets are preprocessed for further processing. The preprocessing phase