A Corpus for mining drug-related knowledge from Twitter chatter: language models and their utilities

Abeed Sarker; Graciela Gonzalez

doi:10.1016/j.dib.2016.11.056

A Corpus for mining drug-related knowledge from Twitter chatter: language models and their utilities

Abeed Sarker^*, Graciela Gonzalez

^*Corresponding author for this work

Research output: Contribution to journal › Article › peer-review

29 Citations (Scopus)

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Abstract

In this data article, we present to the data science, natural language processing and public heath communities an unlabeled corpus and a set of language models. We collected the data from Twitter using drug names as keywords, including their common misspelled forms. Using this data, which is rich in drug-related chatter, we developed language models to aid the development of data mining tools and methods in this domain. We generated several models that capture (i) distributed word representations and (ii) probabilities of n-gram sequences. The data set we are releasing consists of 267,215 Twitter posts made during the four-month period—November, 2014 to February, 2015. The posts mention over 250 drug-related keywords. The language models encapsulate semantic and sequential properties of the texts.

Original language	English
Pages (from-to)	122-131
Number of pages	10
Journal	Data in Brief
Volume	10
DOIs	https://doi.org/10.1016/j.dib.2016.11.056
Publication status	Published - 1 Feb 2017
Externally published	Yes

Bibliographical note

Copyright the Author(s) 2016. Version archived for private and non-commercial use with the permission of the author/s and according to publisher conditions. For further rights please contact the publisher.

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10.1016/j.dib.2016.11.056

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A Corpus for mining drug-related knowledge from Twitter chatter: language models and their utilities

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