Chronic pain affects millions, yet traditional assessments often fail to capture patients’ lived experiences comprehensively. In this study, we used a Motivational Interviewing framework to conduct semi-structured interviews with eleven adults experiencing chronic pain and then applied Natural Language Processing (NLP) to their narratives. We developed an annotation schema that integrates the International Classification of Functioning, Disability, and Health (ICF) with Aspect-Based Sentiment Analysis (ABSA) to convert unstructured narratives into structured representations of key patient experience dimensions. Furthermore, we evaluated whether Large Language Models (LLMs) can automatically extract information using this schema. Our findings advance scalable, patient-centered approaches to chronic pain assessment, paving the way for more effective, data-driven management strategies.