Although clients’ perceptions of therapist empathy are known to correlate with therapy effectiveness, the specific ways that the therapist’s language use contributes to perceived empathy remain less understood. Natural Language Processing techniques, such as transformer models, permit the quantitative, automated, and scalable analysis of therapists’ verbal behaviors. Here, we present a novel approach to extract linguistic features from text-based crisis intervention transcripts to analyze associations between specific crisis counselor verbal behaviors and perceived genuine concern. Linguistic features associated with higher perceived genuine concern included positive emotional language and affirmations; features associated with lower perceived genuine concern included self-oriented talk and overuse of templates. These findings provide preliminary evidence toward pathways for automating real-time feedback to crisis counselors about clients’ perception of the therapeutic relationship.