Mining Health-Related Issues in Consumer Product Reviews by Using Scalable Text Analytics.

Biomedical informatics insights Pub Date : 2016-06-20 eCollection Date: 2016-01-01 DOI:10.4137/BII.S37791
Manabu Torii, Sameer S Tilak, Son Doan, Daniel S Zisook, Jung-Wei Fan
{"title":"Mining Health-Related Issues in Consumer Product Reviews by Using Scalable Text Analytics.","authors":"Manabu Torii,&nbsp;Sameer S Tilak,&nbsp;Son Doan,&nbsp;Daniel S Zisook,&nbsp;Jung-Wei Fan","doi":"10.4137/BII.S37791","DOIUrl":null,"url":null,"abstract":"<p><p>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. </p>","PeriodicalId":88397,"journal":{"name":"Biomedical informatics insights","volume":"8 Suppl 1","pages":"1-11"},"PeriodicalIF":0.0000,"publicationDate":"2016-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.4137/BII.S37791","citationCount":"18","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Biomedical informatics insights","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.4137/BII.S37791","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2016/1/1 0:00:00","PubModel":"eCollection","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 18

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.

Abstract Image

Abstract Image

Abstract Image

查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
使用可扩展文本分析挖掘消费者产品评论中与健康相关的问题。
在我们的大部分生活活动都被数字化和记录的时代,有很多机会可以深入了解人口健康。在线产品评论提供了一个独特的数据源,目前尚未得到充分开发。与健康相关的信息虽然稀缺,但可以在在线产品评论中系统地挖掘出来。利用自然语言处理和机器学习工具,我们能够挖掘130万条与健康相关的杂货产品评论。本研究的目的如下:(1)对消费品审查中发现的健康问题类型进行定量和定性分析;(2)开发机器学习分类器来检测包含健康相关问题的评论;(3)了解文本分析的任务特征和挑战,以指导未来的研究。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 去求助
来源期刊
自引率
0.00%
发文量
0
期刊最新文献
A Data-Driven Approach to Predicting Septic Shock in the Intensive Care Unit A Genome Model to Explain Major Features of Neurodevelopmental Disorders in Newborns. Mathematical Model for Computer-Assisted Modification of Medication Dosing Rules. Applying Supervised Machine Learning to Identify Which Patient Characteristics Identify the Highest Rates of Mortality Post-Interhospital Transfer. Coalitional Game Theory Facilitates Identification of Non-Coding Variants Associated With Autism.
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1