Exploring negative experiences in psychotherapy using an NLP approach on online forum data

Abstract Negative experiences with psychotherapy are common, affecting 3–25% of patients. However, their causes remain underexplored despite their substantial impact on therapy outcomes. Online forums provide unique insights into patients’ concerns due to their anonymity. We collected and anonymized forum posts and used a large language model to identify psychotherapy dissatisfaction. Human raters validated the outputs. To identify and analyze themes, we applied clustering, topic modeling, sentiment analysis, and classification based on an existing meta-analytic framework. In total, we extracted 28,079 text passages reflecting dissatisfaction. Clustering yielded 55 subthemes, covering therapist misbehavior, negative treatment effects, poor alliance, treatment mismatch, and healthcare-related frustrations, extending existing taxonomies. Our NLP-based, mixed-methods approach highlights dissatisfaction as both frequent and multifaceted, surfacing themes often overlooked in traditional research, such as structural barriers and lasting psychological consequences. These findings expand previous frameworks and underscore the need for better recognition of negative therapy experiences.