Depression is a common and often untreated mental disorder that many people who may not seek professional help from a doctor or counselor. The application of NLP in detecting depression from digital conversations such as social media posts, online forums, and text-based communications is made possible by recent developments in Natural Language Processing (NLP). In this paper, we aim to determine the feasibility of using NLP to recognize signs of depression through the identification of linguistic markers and behavioral patterns. To highlight the potential of sentiment analysis, emotion detection, and topic modelling in the detection of early signs of depression, we present a review of the application of NLP on various forms of digital text. In this paper, through case studies and experiments with current NLP models, we show how language use, vocabulary, syntax, and emotional prosody can be used to detect depression with relatively high accuracy. Moreover, the present work discusses the ethical and privacy issues arising from the use of digital text in mental health assessments. Finally, the paper outlines the future perspective of NLP in enhancing depression detection, describing a non-invasive, scalable, and timely approach to mental health monitoring that can be used to supplement conventional diagnosis and enhance prevention efforts.The article is to explores the use of Natural Language Processing (NLP) to detect depression through digital conversations such as social media posts and text-based communications. By analyzing linguistic markers, behavioral patterns, sentiment, emotion, and topic modeling, the study demonstrates how NLP can identify early signs of depression with high accuracy. The paper also addresses ethical and privacy concerns related to using digital text for mental health assessments and discusses the future potential of NLP as a non-invasive, scalable tool for supplementing traditional diagnosis and enhancing prevention efforts.
Python, ML, Deep Learning, NLTK, spaCy, Transformers.
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