Natural language processing for mental disorders: an overview

In recent years, there has been a surge in interest in using natural language processing (NLP) applications for clinical psychology and psychiatry. Despite the increased societal, economic, and academic interest, there has been no systematic critical analysis of the recent progress in NLP applications for mental disorders, or of the resources available for training and evaluating such systems.

This chapter addresses this gap through two main contributions. First, it provides an overview of the NLP literature related to mental disorders, with a focus on autism, dyslexia, schizophrenia, depression and mental health in general. We discuss the strengths and shortcomings of current methodologies, specifically focusing on the challenges in obtaining large volumes of high-quality domain-specific data both for English and for lower-resource languages. We also provide a list of datasets publicly available for researchers who would like to develop NLP methods for specific mental disorders, categorized according to relevant criteria such as data source, language, annotation, and size. Our second contribution is a discussion on how to support the application of these methods to various languages and social contexts. This includes recommendations on conducting robust and ethical experiments from a machine learning perspective, and a discussion on how techniques such as cross-lingual transfer learning could be applied within this area.