We present a computational analysis of the language of drug users when talking about their drug experiences. We introduce a new dataset of over 4,000 descriptions of experiences reported by users of four main drug types, and show that we can predict with an F1-score of up to 88% the drug behind a certain experience. We also perform an analysis of the dominant psycholinguistic processes and dominant emotions associated with each drug type, which sheds light on the characteristics of drug users.
Strapparava and Mihalcea REF perform a computational analysis of the language of drug users when talking about their drug experiences.
A Computational Analysis of the Language of Drug Addiction
EACL
Computer Science