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We investigate by training models directly on text testimonials from a diverse 52\u2010drug pharmacopeia.<\/jats:p><\/jats:sec><jats:sec><jats:title>Methods<\/jats:title><jats:p>Using large language models (i.e. BERT) and 11,816 publicly\u2010available testimonials, we predicted 28\u2010dimensions of sentiment across each narrative, and then validated these predictions with adjudication by a clinical psychiatrist. BERT was then fine\u2010tuned to predict biochemical and demographic information from these narratives. Lastly, canonical correlation analysis linked the drugs' receptor affinities with word usage, revealing 11 statistically\u2010significant latent receptor\u2010experience factors, each mapped to a 3D cortical Atlas.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>These methods elucidate a neurobiologically\u2010informed, sequence\u2010sensitive portrait of drug\u2010induced 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 \u201cLove\u201d, DMT and 5\u2010MeO\u2010DMT to \u201cMystical Experiences\u201d and \u201cEntities and Beings\u201d, and other tryptamines to \u201cSurprise\u201d, \u201cCuriosity\u201d and \u201cRealization\".<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusions<\/jats:title><jats:p>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.<\/jats:p><\/jats:sec>","DOI":"10.1002\/hup.2889","type":"journal-article","created":{"date-parts":[[2023,12,20]],"date-time":"2023-12-20T14:16:55Z","timestamp":1703081815000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["Trajectories of sentiment in 11,816 psychoactive narratives"],"prefix":"10.1002","volume":"39","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-0688-2169","authenticated-orcid":false,"given":"Sam Freesun","family":"Friedman","sequence":"first","affiliation":[{"name":"Data Sciences Platform Broad Institute of MIT and Harvard  Cambridge Massachusetts USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Galen","family":"Ballentine","sequence":"additional","affiliation":[{"name":"Department of Psychiatry SUNY Downstate Health Sciences University  Brooklyn New York USA"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"311","published-online":{"date-parts":[[2023,12,20]]},"reference":[{"key":"e_1_2_10_2_1","article-title":"Memory in chains: Modeling primacy and recency effects in memory for order","author":"Altmann","year":"2000","journal-title":"AIB Proceedings"},{"key":"e_1_2_10_3_1","doi-asserted-by":"publisher","DOI":"10.1126\/sciadv.abl6989"},{"key":"e_1_2_10_4_1","doi-asserted-by":"publisher","DOI":"10.1177\/0269881115609019"},{"key":"e_1_2_10_5_1","unstructured":"Berndt D. 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