How accurate are common sentiment and emotion models in therapy data

Off-the-shelf sentiment/emotion models trained on tweets or reviews often underperform on therapy transcripts due to domain shift and the nuance of clinical conversation (e.g., supportive neutrality, reflective paraphrases). Studies on empathy/emotion show construct-definition gaps and low reproducibility; models improve only after task-specific annotation, fine-tuning, and use of conversation structure.

Recent work indicates that fine-tuned LLMs, evaluated with therapy-appropriate metrics (beyond text similarity), can better assess counseling quality (e.g., MI) but still require human oversight. Best practice: avoid generic “positive/negative” sentiment; instead, fine-tune on counseling corpora for empathy, reflections, change talk, and validate against trained human coders with inter-rater benchmarks.

In short, accuracy is acceptable for targeted, therapy-specific labels after adaptation; it is unreliable for generic sentiment without domain tuning.