Quantitative natural language processing markers of psychoactive drug effects: A pre-registered systematic review
Journal of Psychopharmacology February 16, 2025 Sachin Ahuja, Farida Zaher, Lena Palaniyappan 4 citations
A systematic review of studies using natural language processing to analyze speech and text after psychoactive drug use found that all studied substances—stimulants, MDMA, cannabis, ketamine, and psychedelics—alter language production. Emerging patterns include increased verbosity with stimulants, reduced lexicon with LSD, increased semantic proximity to emotional words with MDMA, increased positive sentiment with psilocybin, and altered speech acoustics with cannabis. Only one study provided externally validated support for identifying MDMA intoxication using NLP and machine learning. Meta-analysis was not possible due to heterogeneity and few studies. The authors call for standardized speech tasks and a shared language corpus to improve replicability.