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dc.contributor.authorIsa, H.*
dc.contributor.authorTrundle, Paul R.*
dc.contributor.authorNeagu, Daniel*
dc.date.accessioned2016-11-23T18:25:39Z
dc.date.available2016-11-23T18:25:39Z
dc.date.issued2014
dc.identifier.citationIsa H, Trundle PR and Neagu D (2014) Social media analysis for product safety using text mining and sentiment analysis. In: Proceedings of the 14th UK Workshop on Computational Intelligence (UKCI). 8-10 Sep 2014, Bradford, UK: 1-7.
dc.identifier.urihttp://hdl.handle.net/10454/10652
dc.descriptionNo
dc.description.abstractThe growing incidents of counterfeiting and associated economic and health consequences necessitate the development of active surveillance systems capable of producing timely and reliable information for all stake holders in the anti-counterfeiting fight. User generated content from social media platforms can provide early clues about product allergies, adverse events and product counterfeiting. This paper reports a work in progress with contributions including: the development of a framework for gathering and analyzing the views and experiences of users of drug and cosmetic products using machine learning, text mining and sentiment analysis; the application of the proposed framework on Facebook comments and data from Twitter for brand analysis, and the description of how to develop a product safety lexicon and training data for modeling a machine learning classifier for drug and cosmetic product sentiment prediction. The initial brand and product comparison results signify the usefulness of text mining and sentiment analysis on social media data while the use of machine learning classifier for predicting the sentiment orientation provides a useful tool for users, product manufacturers, regulatory and enforcement agencies to monitor brand or product sentiment trends in order to act in the event of sudden or significant rise in negative sentiment.
dc.relation.isreferencedbyhttps://doi.org/10.1109/UKCI.2014.6930158
dc.subjectNaive Bayes; Text mining; Sentiment analysis; Product safety; Social media; Machine learning
dc.subject.classificationYes
dc.titleSocial media analysis for product safety using text mining and sentiment analysis
dc.typeConference Paper
dc.type.versionNo full-text in the repository


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