A Case Study: Detecting Counselor Reflections in Psychotherapy for Addictions using Linguistic Features

Motivational Interviewing (MI) is a goal-oriented psychotherapy, employed in cases such as addiction, which helps clients (i.e., patients) explore and resolve their ambivalence about the problem at hand in a dialog setting. Measuring the counselor's proficiency with MI has typically been assessed via behavioral coding - a time consuming, non-technological approach. This paper examines a computational approach to assessing the quality of MI. Specifically, we focus on a particular aspect of the counselor behavior reflections believed to be a critical indicator of MI therapy quality. We automatically tag reflection instances in a maximum entropy Markov modeling framework using several linguistic features with rich contextual information obtained from the session transcripts. We achieve an F-score of over 80% while gaining insight about the information sources as perceived by the trained annotators.