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.

中文翻译：

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利用自然语言处理检测精神障碍的调查：文献综述、趋势和挑战


多年来，科学界一直在研究检测某些精神障碍和危险行为的监测方法，例如抑郁症、饮食失调、赌博和自杀意念等，以便启动预防或缓解策略，在严重的情况下，采取临床治疗治疗。自然语言处理是处理精神障碍自动检测最活跃的学科之一。本文对自然语言处理应用于识别某些精神障碍的研究工作进行了全面而广泛的回顾。为此，我们从文献综述中确定了用于表示文本的主要特征类型、首选的机器学习算法或最具针对性的社交媒体平台等。此外，该论文还报道了科学论坛和项目，重点关注在最流行的社交网络上自动检测这些问题。因此，本汇编提供了对该问题的广泛看法，总结了主要策略和重要发现，但也认识到迄今为止发表的研究著作中的一些弱点，为未来的研究提供了线索。