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Online Shopping Behavior Study Based on Multi-granularity Opinion Mining: China Versus America

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Abstract

With the development of e-commerce, many products are now being sold worldwide, and manufacturers are eager to obtain a better understanding of customer behavior in various regions. To achieve this goal, most previous efforts have focused mainly on questionnaires, which are time-consuming and costly. The tremendous volume of product reviews on e-commerce Web sites has seen a new trend emerge, whereby manufacturers attempt to understand user preferences by analyzing online reviews. Following this trend, this paper addresses the problem of studying customer behavior by exploiting recently developed opinion mining techniques. This work is novel for three reasons. First, questionnaire-based investigation is automatically enabled by employing algorithms for template-based question generation and opinion mining-based answer extraction. Using this system, manufacturers are able to obtain reports of customer behavior featuring a much larger sample size, more direct information, a higher degree of automation, and a lower cost. Second, international customer behavior study is made easier by integrating tools for multilingual opinion mining. Third, this is the first time an automatic questionnaire investigation has been conducted to compare customer behavior in China and America, where product reviews are written and read in Chinese and English, respectively. Our study on digital cameras, smartphones, and tablet computers yields three findings. First, Chinese customers follow the Doctrine of the Mean and often use euphemistic expressions, while American customers express their opinions more directly. Second, Chinese customers care more about general feelings, while American customers pay more attention to product details. Third, Chinese customers focus on external features, while American customers care more about the internal features of products.

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Notes

  1. http://www.camarketing.com.

  2. We select the brands with the most two quantity of reviews from Amazon as our data set.

  3. https://catalog.ldc.upenn.edu/LDC2002L27

  4. Overall sentiment classification here is similar to document-level sentiment classification.

  5. https://github.com/fxsjy/jieba.

  6. https://github.com/fxsjy/jieba.

  7. http://www.nltk.org/.

  8. http://ir.dlut.edu.cn/EmotionOntologyDownload.aspx?utm_source=weibolife.

  9. https://hlt.fbk.eu/technologies/sentiwords.

  10. http://radimrehurek.com/gensim/.

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Acknowledgments

This work is supported by the Major Projects of National Social Science Fund (No. 13&ZD174), the National Social Science Fund Project (No. 14BTQ033), the Natural Science Foundation of China (Nos. 61305090, 61272233), and the Opening Foundation of Alibaba Research Center for Complex Sciences, Hangzhou Normal University (No. PD12001003002003). We thank the reviewers for the insightful comments.

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Authors and Affiliations

  1. Department of Information Management, Nanjing University of Science and Technology, Nanjing, 210094, China

    Qingqing Zhou & Chengzhi Zhang

  2. Alibaba Research Center for Complex Sciences, Hangzhou Normal University, Hangzhou, 311121, China

    Qingqing Zhou & Chengzhi Zhang

  3. School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing, 210094, China

    Rui Xia

  4. Jiangsu Key Laboratory of Data Engineering and Knowledge Service, Nanjing University, Nanjing, 210093, China

    Chengzhi Zhang

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Correspondence to Chengzhi Zhang.

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Zhou, Q., Xia, R. & Zhang, C. Online Shopping Behavior Study Based on Multi-granularity Opinion Mining: China Versus America. Cogn Comput 8, 587–602 (2016). https://doi.org/10.1007/s12559-016-9384-x

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