Predicting therapist empathy in motivational interviews using language features inspired by psycholinguistic norms

Therapist language plays a critical role in influencing the overall quality of psychotherapy. Notably, it is a major contributor to the perceived level of empathy expressed by therapists, a primary measure for judging their efficacy. We explore psycholinguistics inspired features for predicting therapist empathy. These features model language which conveys information about affective and cognitive processes, which is central to the therapist expressing understanding of the patient’s perspective. We describe the dimensional features obtained based on psycholinguisitic norms, and their application to predicting empathy expressed in motivational interviewing sessions for addiction counseling. We compare these to standard lexical features (n-grams) and demonstrate that these features contain complementary information for predicting therapist empathy. The highest empathy prediction results achieved are 75.28% UAR and 0.6112 Spearman’s correlation.