Monitoring of Mental Illnesses Based on Natural Language Processing

Abstract

With the development of the Internet, more people are beginning to share their feelings and thoughts online. At the same time, the mental health issues of modern people are becoming increasingly prominent, gradually becoming a major source of suffering in people’s lives and affecting the health and well-being of society. Mental illness is a complex, multifactorial disease associated with individual risk factors as well as various socioeconomic and clinical correlations. Therefore, the scientific community has developed a series of mental illness detection methods, among which natural language processing (NLP) plays a crucial role in mental illness detection. However, despite its rapid development in mental illness detection, the current detection methods still have some limitations. It cannot judge the authenticity of online information. Meanwhile, most detection models are only based on keyword analysis, resulting in overly simplistic evaluation criteria and a tendency to lead to some prejudgments. This paper mainly discusses the current methods and limitations of NLP models in mental illness detection, as well as possible solutions, aiming to provide some clues and assistance for future research.