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

Abstract

BackgroundTwitter is rich in data for text and data analytics research, with the ability to capture trends.ObjectivesThis 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.MethodsTwitter 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.ResultsMore 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.DiscussionThe case study presents text‐mining applications relevant to help make informed decisions in library practice through service analysis, quality analysis, and collection analysis.ConclusionsFindings 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.

Capítulo 3. Inteligencia Artificial en la comunicación científica

“…Elicit, según Kung (2023) permite encontrar artículos relevantes, resumir las conclusiones y extraer información clave. Facilita filtrar temas del resumen, incluida una versión abreviada del resumen, los resultados, el número de participantes, el resumen de la población, etc.…”

“…Extrae información detallada del artículo, incluidos conceptos clave, sinopsis del texto completo, resumen académico, análisis comparativo, etc. (Kung, 2023). Los ejemplos expuestos ponen en evidencia la rápida expansión y adopción de tecnologías basadas en inteligencia artificial.…”