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Natural language processing in mental health applications using non-clinical texts

  • RAFAEL A. CALVO (a1), DAVID N. MILNE (a1), M. SAZZAD HUSSAIN (a1) (a2) and HELEN CHRISTENSEN (a3)
Abstract
Abstract

Natural language processing (NLP) techniques can be used to make inferences about peoples’ mental states from what they write on Facebook, Twitter and other social media. These inferences can then be used to create online pathways to direct people to health information and assistance and also to generate personalized interventions. Regrettably, the computational methods used to collect, process and utilize online writing data, as well as the evaluations of these techniques, are still dispersed in the literature. This paper provides a taxonomy of data sources and techniques that have been used for mental health support and intervention. Specifically, we review how social media and other data sources have been used to detect emotions and identify people who may be in need of psychological assistance; the computational techniques used in labeling and diagnosis; and finally, we discuss ways to generate and personalize mental health interventions. The overarching aim of this scoping review is to highlight areas of research where NLP has been applied in the mental health literature and to help develop a common language that draws together the fields of mental health, human-computer interaction and NLP.

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This is an Open Access article, distributed under the terms of the Creative Commons Attribution licence (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted re-use, distribution, and reproduction in any medium, provided the original work is properly cited.
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RAC and SH are supported by the Young and Well Cooperative Research Centre, which is established under the Australian Government’s Cooperative Research Centres Program. RAC is supported by an Australian Research Council Future Fellowship FT140100824. RAC and DM are supported by an Australian Research Council Linkage Project. HC is supported by an NHMRC Fellowship 1056964.

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Natural Language Engineering
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