The quality of a counseling intervention relies highly on the active collaboration between clients and counselors. In this paper, we explore several linguistic aspects of the collaboration process occurring during counseling conversations. Specifically, we address the differences between high-quality and low-quality counseling. Our approach examines participants’ turn-by-turn interaction, their linguistic alignment, the sentiment expressed by speakers during the conversation, as well as the different topics being discussed. Our results suggest important language differences in low- and high-quality counseling, which we further use to derive linguistic features able to capture the differences between the two groups. These features are then used to build automatic classifiers that can predict counseling quality with accuracies of up to 88 %.

Counselor empathy is associated with better outcomes in psychology and behavioral counseling. In this paper, we explore several aspects pertaining to counseling interaction dynamics and their relation to counselor empathy during motivational interviewing encounters. Particularly, we analyze aspects such as participants’ engagement, participants’ verbal and nonverbal accommodation, as well as topics being discussed during the conversation, with the final goal of identifying linguistic and acoustic markers of counselor empathy. We also show how we can use these findings alongside other raw linguistic and acoustic features to build accurate counselor empathy classifiers with accuracies of up to 80 %.

As the number of people receiving psycho-therapeutic treatment increases, the automatic evaluation of counseling practice arises as an important challenge in the clinical domain. In this paper, we address the automatic evaluation of counseling performance by analyzing counselors’ language during their interaction with clients. In particular, we present a model towards the automation of Motivational Interviewing (MI) coding, which is the current gold standard to evaluate MI counseling. First, we build a dataset of hand labeled MI encounters ; second, we use text-based methods to extract and analyze linguistic patterns associated with counselor behaviors ; and third, we develop an automatic system to predict these behaviors. We introduce a new set of features based on semantic information and syntactic patterns, and show that they lead to accuracy figures of up to 90 %, which represent a significant improvement with respect to features used in the past.