Semantics enabled role based sentiment analysis for drug abuse on social media: A framework

Raja, Bilal Saeed; Ali, Ahmad; Ahmed, Mansoor; Khan, Abid; Malik, Atif Parvaiz

doi:10.1109/iscaie.2016.7575064

Cited by 6 publications

(1 citation statement)

References 16 publications

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“…In the medical and health arenas, sentiment analysis and opinion mining methods are not used as often and are mostly applied to track public opinions on issues such as drug addiction (Raja et al 2016) or very general public health issues (Palomino et al 2016;Yang, Lee & Kuo 2016), all using keywords as trackers.…”

Section: Sentiment Analysis and Opinion Miningmentioning

confidence: 99%

An Initiative to Track Sentiments in Altmetrics

Halevi

Schimming

2018

joa

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A recent survey from Pew Research Center (NW, Washington & Inquiries 2018) found that over 44 million people receive science-related information from social media channels to which they subscribe. These include a variety of topics such as new discoveries in health sciences as well as "news you can use" information with practical tips (p. 3). Social and news media attention to scientific publications has been tracked for almost a decade by several platforms which aggregate all public mentions of and interactions with scientific publications. Since the amount of comments, shares, and discussions of scientific publications can reach an audience of thousands, understanding the overall "sentiment" towards the published research is a mammoth task and typically involves merely reading as many posts, shares, and comments as possible. This paper describes an initiative to track and provide sentiment analysis to large social and news media mentions and label them as "positive", "negative" or "neutral". Such labels will enable an overall understanding of the content that lies within these social and news media mentions of scientific publications.

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Section: Sentiment Analysis and Opinion Miningmentioning

confidence: 99%