The Application of Natural Language Processing in the Detection and Intervention of Mental Health Fields

Abstract

Mental health disorders have become a prominent public health problem worldwide. Traditional mental health services have many deficiencies in diagnostic criteria, resource allocation, and other aspects. The development of Natural Language Processing (NLP) technology offers solutions to solve these challenges. This article conducts a systematic review of the application of NLP in the field of mental health over the past five years. Taking text data as the core research object, it sorts out the application situations such as disease detection and special scenario analysis. It compares the performance and applicable scenarios of three technical routes: rule-based, traditional machine learning, and deep learning. Point out the shortcomings of the current research in terms of language diversity, clinical adaptability, multimodal fusion framework, and ethical and legal aspects. Compared with existing reviews, this article focuses on the perspective of cross-integration between technology and clinical practice, clarifying future research directions such as the development of cross-language models and the construction of a "text + physiological signal" integration framework, with the aim of providing ideas for the intelligent development of mental health services.