Depression Detection From Social Media Text Using Embedding-Enhanced Long Short-Term Memory and Recurrent Neural Network Models

Depression is a common neuropsychological condition that has a considerable influence on emotional stability, daily functioning, and general quality of life in various populations. Prompt detection of depressive symptoms is important for intervening effectively and reducing the chances of severe psychiatric effects. As digital platforms have rapidly expanded, especially social media, access to large volumes of user-generated textual content has provided new opportunities for automated mental health assessment. The current research paper discusses the results of state-of-the-art deep learning-based methods for early-stage depression detection on social media text. Recurrent neural networks (RNNs), long short-term memory (LSTM) models, and their embedding-based counterparts are built and trained on curated datasets of depressive and non-depressive posts. The methodology focuses on the derivation of linguistic and contextual signs that indicate underlying emotional conditions. It has been shown that embedding-enhanced deep learning models perform better than traditional methods, and that LSTM-based models yield ideal results in capturing and predicting temporal relationships and subtle emotional differences. The results show that there is potential to integrate deep learning algorithms into real-time mental health monitoring systems to assist clinicians and researchers in the early detection of depression.