Patient Voice and Treatment Nonadherence in Cancer Care: A Scoping Review of Sentiment Analysis

Background: Treatment nonadherence in oncology is common. Surveys often miss why patients do not follow recommendations. We synthesised Natural Language Processing (NLP) studies, mainly sentiment analysis, of patient-generated content (social media, forums, blogs, review sites, and survey free text) to identify communication and relationship factors linked to nonadherence and concordance. Methods: We conducted a scoping review (PRISMA-ScR). Searches of PubMed, CINAHL, and Scopus from 2013 to 15 June 2024 identified eligible studies. We included 25 studies. Data were charted by source, cancer type, NLP technique, and adherence/concordance indicators, then synthesised via discourse analysis and narrative synthesis. Results: Four themes emerged: (1) unmet emotional needs; (2) suboptimal information and communication; (3) unclear concordance within person-centred care; and (4) misinformation dynamics and perceived clinician bias. Sentiment analysis helped identify emotions and information gaps that surveys often miss. Conclusions: Patient-voice data suggest practical actions for nursing, including routine distress screening, teach-back, misinformation countermeasures, and explicit concordance checks to improve adherence and shared decision making. Registration: Not registered.