Hybrid contextual and sentiment-based machine learning model for identifying depression risk in social media

Depression is a dangerous and widespread mental disorder globally, often leading to feelings of low self-esteem, hopelessness, and suicide. With the rapid development of social media platforms, they have become spaces for people to share experiences and emotions and relieve stress and fatigue. Consequently, detecting depression on social media has become meaningful and consistent with development trends. However, it faces significant challenges due to the unstructured nature of social media data and the complex interaction of linguistic signals, context, and sentiment. In this paper, a novel model for detecting depressive posts on social media is proposed, called CLSDepDet. This model leverages effective feature extraction techniques, combining context, language, and sentiment features to enhance classification performance. We employ the Long Short-Term Memory (LSTM) architecture to capture linguistic and sentiment characteristics, augmented by the Hierarchical Contextual Attention Network (HCAN) to capture contextual information at both the word and sentence levels. Experimental results on a Reddit dataset demonstrate that CLSDepDet outperforms advanced methods, achieving an accuracy of 93 % and an F1 score of 95 %. The proposed model underscores the importance of integrating diverse features to improve classification accuracy and opens avenues for further research in developing efficient deep learning models for mental health applications. CLSDepDet not only provides a novel approach to detecting depressive posts on social media but also contributes to the development of early detection and diagnosis systems for depression, thereby improving the quality of life for affected individuals.