Text mining applications to support health library practice: A case study on marijuana legalization Twitter analytics

Abstract

BACKGROUND: Twitter is rich in data for text and data analytics research, with the ability to capture trends. 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. 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. 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. 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. 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.

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