EXPLAINABLE DETECTION OF DEPRESSION IN SOCIAL MEDIA CONTENTS USING NATURAL LANGUAGE PROCESSING

Abstract

The widespread use of social media platforms provides valuable insights for real-time mental health monitoring, particularly for identifying signs of depression through user-generated content. This project introduces a deep learning-based approach for depression detection that leverages the sequential modeling capabilities of Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) networks. To address the challenges posed by informal language, the preprocessing pipeline includes emoji normalization, slang replacement using a custom dictionary, and the extraction of emotion scores to enhance semantic understanding. The hybrid LSTMGRU model is trained on annotated social media posts, effectively capturing emotional and contextual patterns within the text.