Detecting depression using natural language processing (NLP) techniques

Abstract

Depression is a prevalent mental health disorder with a significant impact on individuals and society. Timely identification and intervention are crucial for effective treatment and support. This study explores the application of Natural Language Processing (NLP) techniques and machine learning algorithms in the detection of depression through the analysis of textual and speech data where Various NLP techniques, including sentiment analysis, language modelling, and emotion detection, are employed to extract meaningful patterns and features indicative of depressive states, We also discuss the approaches used in the models and Advantages and Disadvantages.