@article{ref1,
title="Text mining applications to support health library practice: a case study on marijuana legalization Twitter analytics",
journal="Health information and libraries journal",
year="2023",
author="Kung, Janice Y. and Ly, Kynan and Shiri, Ali",
volume="ePub",
number="ePub",
pages="ePub-ePub",
abstract="BACKGROUND: Twitter is rich in data for text and data analytics research, with the ability to capture trends. <br><br>OBJECTIVES: This study examines Canadian tweets on marijuana legalization and terminology used. Presented as a case study, Twitter analytics will demonstrate the varied applications of how this kind of research method may be used to inform library practice. <br><br>METHODS: Twitter API was used to extract a subset of tweets using seven relevant hashtags. Using open-source programming tools, the sampled tweets were analysed between September to November 2018, identifying themes, frequently used terms, sentiment, and co-occurring hashtags. <br><br>RESULTS: More than 1,176,000 tweets were collected. The most popular hashtag co-occurrence, two hashtags appearing together, was #cannabis and #CdnPoli. There was a high variance in the sentiment analysis of all collected tweets but most scores had neutral sentiment. <br><br>DISCUSSION: The case study presents text-mining applications relevant to help make informed decisions in library practice through service analysis, quality analysis, and collection analysis. <br><br>CONCLUSIONS: Findings from sentiment analysis may determine usage patterns from users. There are several ways in which libraries may use text mining to make evidence-informed decisions such as examining all possible terminologies used by the public to help inform comprehensive evidence synthesis projects and build taxonomies for digital libraries and repositories.<p /> <p>Language: en</p>",
language="en",
issn="1471-1834",
doi="10.1111/hir.12473",
url="http://dx.doi.org/10.1111/hir.12473"
}