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Citation |
Torii M, Tilak SS, Doan S, Zisook DS, Fan JW. Biomed. Inform. Insights 2016; 8(Suppl 1): 1-11. |
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Affiliation |
Medical Informatics, Kaiser Permanente Southern California, San Diego, CA, USA. |
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Copyright |
(Copyright © 2016, Libertas Academica) |
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DOI |
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PMID |
27375358 |
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Abstract |
In an era when most of our life activities are digitized and recorded, opportunities abound to gain insights about population health. Online product reviews present a unique data source that is currently underexplored. Health-related information, although scarce, can be systematically mined in online product reviews. Leveraging natural language processing and machine learning tools, we were able to mine 1.3 million grocery product reviews for health-related information. The objectives of the study were as follows: (1) conduct quantitative and qualitative analysis on the types of health issues found in consumer product reviews; (2) develop a machine learning classifier to detect reviews that contain health-related issues; and (3) gain insights about the task characteristics and challenges for text analytics to guide future research. Language: en |