Assessing affective dimensions of play in psychodynamic child psychotherapy via text analysis

Assessment of emotional expressions of young children during clinical work is an important, yet arduous task. Especially in natural play scenarios, there are not many constraints on the behavior of the children, and the expression palette is rich. There are many approaches developed for the automatic analysis of affect, particularly from facial expressions, paralinguistic features of the voice, as well as from the myriads of non-verbal signals emitted during interactions. In this work, we describe a tool that analyzes verbal interactions of children during play therapy. Our approach uses natural language processing techniques and tailors a generic affect analysis framework to the psychotherapy domain, automatically annotating spoken sentences on valence and arousal dimensions. We work with Turkish texts, for which there are far less natural language processing resources than English, and our approach illustrates how to rapidly develop such a system for non-English languages. We evaluate our approach with longitudinal psychotherapy data, collected and annotated over a one year period, and show that our system produces good results in line with professional clinicians’ assessments.