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    <abstract>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.</abstract>
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%0 Conference Proceedings
%T A Computational Analysis of the Language of Drug Addiction
%A Strapparava, Carlo
%A Mihalcea, Rada
%S Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 2, Short Papers
%D 2017
%8 apr
%I Association for Computational Linguistics
%C Valencia, Spain
%F strapparava-mihalcea-2017-computational
%X 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.
%U https://aclanthology.org/E17-2022
%P 136-142

Markdown (Informal)

[A Computational Analysis of the Language of Drug Addiction](https://aclanthology.org/E17-2022) (Strapparava & Mihalcea, EACL 2017)

• A Computational Analysis of the Language of Drug Addiction (Strapparava & Mihalcea, EACL 2017)

ACL

• Carlo Strapparava and Rada Mihalcea. 2017. A Computational Analysis of the Language of Drug Addiction. In Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 2, Short Papers, pages 136–142, Valencia, Spain. Association for Computational Linguistics.