Trajectories of sentiment in 11,816 psychoactive narratives

Machine learning models trained on 11,816 publicly available testimonials about 52 different drugs can predict and quantify subjective experiences. The models identified 11 statistically significant latent factors linking drug receptor affinities to word usage, which mapped to brain regions. A pervasive distinction emerged between universal psychedelic experiences of heightened feeling and the grim, mundane experiences of addiction and mental illness. MDMA was linked to “Love”, DMT and 5-MeO-DMT to “Mystical Experiences” and “Entities and Beings”, and other tryptamines to “Surprise”, “Curiosity”, and “Realization”. The methods show potential for characterizing psychoactivity through data-driven sentiment analysis.

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Abstract

Abstract Objective Can machine learning (ML) enable data‐driven discovery of how changes in sentiment correlate with different psychoactive experiences? We investigate by training models directly on text testimonials from a diverse 52‐drug pharmacopeia. Methods Using large language models (i.e. BERT) and 11,816 publicly‐available testimonials, we predicted 28‐dimensions of sentiment across each narrative, and then validated these predictions with adjudication by a clinical psychiatrist. BERT was then fine‐tuned to predict biochemical and demographic information from these narratives. Lastly, canonical correlation analysis linked the drugs' receptor affinities with word usage, revealing 11 statistically‐significant latent receptor‐experience factors, each mapped to a 3D cortical Atlas. Results These methods elucidate a neurobiologically‐informed, sequence‐sensitive portrait of drug‐induced subjective experiences. The models' results converged, revealing a pervasive distinction between the universal psychedelic heights of feeling in contrast to the grim, mundane, and personal experiences of addiction and mental illness. Notably, MDMA was linked to “Love”, DMT and 5‐MeO‐DMT to “Mystical Experiences” and “Entities and Beings”, and other tryptamines to “Surprise”, “Curiosity” and “Realization". Conclusions ML methods can create unified and robust quantifications of subjective experiences with many different psychoactive substances and timescales. The representations learned are evocative and mutually confirmatory, indicating great potential for ML in characterizing psychoactivity.