ArticleAlcohol and alcoholism (Oxford, Oxfordshire)2026
Natural language processing of patient in-session speech to predict brief motivational interviewing alcohol intervention response: an exploratory study.
Article in Alcohol and alcoholism (Oxford, Oxfordshire), 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
backgroundMotivational interviewing (MI) is a patient-centred, goal-oriented psychotherapy for alcohol use disorder (AUD) and numerous other conditions. While some language patterns have been linked to MI response, less is known about how broader linguistic features, sentiment, and engagement relate to post-intervention drinking. This study used natural language processing to examine these associations and clarify mechanisms through which MI for AUD exerts its effects.
methodsAdults with AUD (N = 68) completed a single MI session with structured feedback and discussion of potential drinking changes. Speech transcripts were analysed for change-, emotion-, motivation-, substance-, and health-related words. Sentiment analysis assessed emotional polarity, and engagement was measured by total words spoken. Linear regression models tested associations between linguistic features and drinking outcomes, including drinks/week, percent heavy drinking days (%HDD), and percent drinking days (%DD).
resultsParticipants showed significant reductions in drinks per week and %DD. Consistent with recent reviews of MI predictors, linguistic analyses found that greater use of change talk and health-related language was associated with more drinks per week at follow-up, whereas greater emotional talk and positive sentiment predicted fewer drinks per week. Health- and substance-related language predicted higher %HDD, while social motivation-related language predicted lower %DD. Term-frequency and multivariate analyses supported these patterns.
conclusionsVia natural language processing of MI speech, linguistic features such as motivational content and sentiment were linked to drinking outcomes. Findings demonstrate the potential of this approach as a scalable, data-driven complement to traditional coding systems, with applications for real-time feedback, clinician training, and personalized interventions.
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