title: Mining Health-Related Issues in Consumer Product Reviews by Using Scalable Text Analytics.
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.
authors.affiliation: Medical Informatics, Kaiser Permanente Southern California, San Diego, CA, USA.
authors.forename: Manabu
authors.initials: M
authors.lastname: Torii
authors.affiliation: Medical Informatics, Kaiser Permanente Southern California, San Diego, CA, USA.
authors.forename: Sameer S
authors.initials: SS
authors.lastname: Tilak
authors.affiliation: Medical Informatics, Kaiser Permanente Southern California, San Diego, CA, USA.
authors.forename: Son
authors.initials: S
authors.lastname: Doan
authors.affiliation: Medical Informatics, Kaiser Permanente Southern California, San Diego, CA, USA.
authors.forename: Daniel S
authors.initials: DS
authors.lastname: Zisook
authors.affiliation: Medical Informatics, Kaiser Permanente Southern California, San Diego, CA, USA.
authors.forename: Jung-Wei
authors.initials: JW
authors.lastname: Fan
keywords: big data; consumer health informatics; natural language processing; online product reviews; syndromic surveillance; text mining