EXPLORING THE ROLE OF NATURAL LANGUAGE PROCESSING IN MENTAL HEALTH CARE: A REVIEW ARTICLE

Abstract

Natural Language Processing (NLP) is revolutionizing mental health care, transforming how conditions are diagnosed, treated, and monitored. By analyzing vast volumes of written and spoken language from patients, NLP equips clinicians with tools to identify mental health indicators that might be too subtle to detect through conventional methods. Through techniques like sentiment analysis, it can track emotional trends and linguistic cues over time—revealing signs of disorders such as depression, anxiety, PTSD, and others. Moreover, NLP-powered chatbots and virtual therapy platforms offer 24/7 support, facilitating early assessments, guided mindfulness practices, and even live, anonymous conversations in a safe, stigma-free environment. These innovations significantly improve access to mental health care, particularly for individuals facing geographic, economic, or societal obstacles. However, the use of NLP in this sensitive domain raises important ethical questions. Ensuring privacy, obtaining informed consent, and addressing potential algorithmic biases are critical challenges that must be managed responsibly. Nevertheless, the future of NLP in mental health appears promising—with potential for even more tailored, empathetic interventions, proactive crisis prevention, and holistic support systems.