Using decision trees to characterize verbal communication during change and stuck episodes in the therapeutic process

Abstract

Methods are needed for creating models to characterize verbal communication between therapists and their patients that are suitable for teaching purposes without losing analytical potential. A technique meeting these twin requirements is proposed that uses decision trees to identify both change and stuck episodes in therapist-patient communication. Three decision tree algorithms (C4.5, NBTree, and REPTree) are applied to the problem of characterizing verbal responses into change and stuck episodes in the therapeutic process. The data for the problem is derived from a corpus of 8 successful individual therapy sessions with 1760 speaking turns in a psychodynamic context. The decision tree model that performed best was generated by the C4.5 algorithm. It delivered 15 rules characterizing the verbal communication in the two types of episodes. Decision trees are a promising technique for analyzing verbal communication during significant therapy events and have much potential for use in teaching practice on changes in therapeutic communication. The development of pedagogical methods using decision trees can support the transmission of academic knowledge to therapeutic practice.

Supervised Speaker Diarization Using Random Forests: A Tool for Psychotherapy Process Research

“…The presented method for supervised speaker diarization in dyadic psychotherapy is based on a random forest algorithm. While machine learning methods in general have gained attention in psychological research (Orrù et al, 2020), random forests can be considered a rather understandable machine learning algorithm that has already found its way into psychotherapy research (Imel et al, 2015;Masías et al, 2015;Husain et al, 2016;Sun et al, 2017;Wallert et al, 2018;Zilcha-Mano, 2019;Rubel et al, 2020;Zimmermann et al, 2020). The random forest algorithm is a machine learning classifier based on decision trees (Kotsiantis, 2013).…”