Linguistic indicators of severity and profess in online text-based therapy for depression
Association for Computational Linguistics
Copyright 2014 Association for Computational Linguistics
Mental illnesses such as depression and anxiety are highly prevalent, and therapy is increasingly being offered online. This new setting is a departure from face-to-face therapy, and offers both a challenge and an opportunity – it is not yet known what features or approaches are likely to lead to successful outcomes in such a different medium, but online text-based therapy provides large amounts of data for linguistic analysis. We present an initial investigation into the application of computational linguistic techniques, such as topic and sentiment modelling, to online therapy for depression and anxiety. We find that important measures such as symptom severity can be predicted with comparable accuracy to face-to-face data, using general features such as discussion topic and sentiment; however, measures of patient progress are captured only by finer-grained lexical features, suggesting that aspects of style or dialogue structure may also be important.
This work was partly supported by the ConCreTe project. The project ConCreTe acknowledges the financial support of the Future and Emerging Technologies (FET) programme within the Seventh Framework Programme for Research of the European Commission, under FET grant number 611733.
Proceedings of the ACL workshop on Computational Linguistics and Clinical Psychology: From Linguistic Signal to Clinical Reality, pages 7–16, Baltimore, Maryland USA, June 27, 2014. Copyright 2014 Association for Computational Linguistics
Proceedings of the ACL workshop on Computational Linguistics and Clinical Psychology (CLCP), Baltimore, Maryland USA, June 27, 2014.