Name
Abstract | ||
|---|---|---|
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%. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.18653/v1/P17-1131 | PROCEEDINGS OF THE 55TH ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS (ACL 2017), VOL 1 |
Field | DocType | Volume |
Computer science,Natural language processing,Artificial intelligence,Counseling therapy,Psychotherapist | Conference | P17-1 |
Citations | PageRank | References |
5 | 0.57 | 7 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Verónica Pérez-Rosas | 1 | 40 | 5.02 |
| Rada Mihalcea | 2 | 6460 | 445.54 |
| Kenneth Resnicow | 3 | 6 | 1.66 |
| Satinder P. Singh | 4 | 5508 | 715.52 |
| Lawrence C. An | 5 | 11 | 1.88 |