Analyzing the Language of Therapist Empathy in Motivational Interview based Psychotherapy.
Empathy is an important aspect of social communication, especially in medical and psychotherapy applications. Measures of empathy can offer insights into the quality of therapy. We use an N-gram language model based maximum likelihood strategy to classify empathic versus non-empathic utterances and report the precision and recall of classification for various parameters. High recall is obtained with unigram while bigram features achieved the highest F1-score. Based on the utterance level models, a group of lexical features are extracted at the therapy session level. The effectiveness of these features in modeling session level annotator perceptions of empathy is evaluated through correlation with expert-coded session level empathy scores. Our combined feature set achieved a correlation of 0.558 between predicted and expert-coded empathy scores. Results also suggest that the longer term empathy perception process may be more related to isolated empathic salient events.
Department of Electrical Engineering, University of Southern California, Los Angeles, CA, U.S.A.
Bo
B
Xiao
Department of Computer Science, University of Southern California, Los Angeles, CA, U.S.A.
Dogan
D
Can
Department of Electrical Engineering, University of Southern California, Los Angeles, CA, U.S.A.
Panayiotis G
PG
Georgiou
Department of Psychiatry & Behavioral Sciences, University of Washington, Seattle, WA, U.S.A.
David
D
Atkins
Department of Electrical Engineering, University of Southern California, Los Angeles, CA, U.S.A; Department of Computer Science, University of Southern California, Los Angeles, CA, U.S.A.
Shrikanth S
SS
Narayanan
Empathy; Language Model; Motivational Interview