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Customer knowledge discovery from online reviews

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Abstract

The explosive growth of Chinese electronic market has made it possible for companies to better understand consumers’ opinion towards their products in a timely fashion through their online reviews. This study proposes a framework for extracting knowledge from online reviews through text mining and econometric analysis. Specifically, we extract product features, detect topics, and identify determinants of customer satisfaction. An experiment on the online reviews from a Chinese leading B2C (Business-to-Customer) website demonstrated the feasibility of the proposed method. We also present some findings about the characteristics of Chinese reviewers.

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  1. http://www.newegg.com/Product/Product.aspx?Item=75-205-220&SortField=0&SummaryType=0&Pagesize=10&PurchaseMark=&SelectedRating=-1&VideoOnlyMark=False&VendorMark=&IsFeedbackTab=true&Keywords=%28keywords%29&Page=1#scrollFullInfo

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Acknowledgment

The research is supported by the Beijing Forestry University Young Scientist Fund (No. BLX201127 and YSE2011-8), National Natural Science Foundation of China under Grant No. 90924020, 71101153, and 70971005, the PhD Program Foundation of Education Ministry of China under Contract No. 200800060005, and Alibaba Young Researcher Funding under Contract No. Ali-2010-B-6.

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Authors and Affiliations

  1. School of Economics and Management, Beijing Forestry University, 35 East Qinghua Road, Haidian District, Beijing, 100083, People’s Republic of China

    Weijia You

  2. Department of OMIS, Leavey School of Business, Santa Clara University, Santa Clara, CA, 95053, USA

    Mu Xia

  3. School of Economics and Management, Beihang University, Beijing, 100191, China

    Lu Liu & Dan Liu

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  1. Weijia You
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  2. Mu Xia
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Correspondence to Mu Xia.

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Responsible Editor: Xin Luo

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You, W., Xia, M., Liu, L. et al. Customer knowledge discovery from online reviews. Electron Markets 22, 131–142 (2012). https://doi.org/10.1007/s12525-012-0098-y

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