A survey on detecting mental disorders with natural language processing: Literature review, trends and challenges

Abstract

For years, the scientific community has researched monitoring approaches for the detection of certain mental disorders and risky behaviors, like depression, eating disorders, gambling, and suicidal ideation among others, in order to activate prevention or mitigation strategies and, in severe cases, clinical treatment. Natural Language Processing is one of the most active disciplines dealing with the automatic detection of mental disorders. This paper offers a comprehensive and extensive review of research works on Natural Language Processing applied to the identification of some mental disorders. To this end, we have identified from a literature review, which are the main types of features used to represent the texts, the machine learning algorithms that are preferred or the most targeted social media platforms, among other aspects. Besides, the paper reports on scientific forums and projects focused on the automatic detection of these problems over the most popular social networks. Thus, this compilation provides a broad view of the matter, summarizing main strategies, and significant findings, but, also, recognizing some of the weaknesses in the research works published so far, serving as clues for future research. • Detection of mental disorders and risk behaviors through social networks. • Updated review of methods, features and algorithms in the automatic detection. • Compilation of forums and scientific projects focused on the automatic detection. • Some major challenges identified are a lack of systems for languages other than English, expert annotation of corpora, and applicability in real world solutions.

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