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The user preference identification for product improvement based on online comment patch

Author

Listed:
  • Shugang Li

    (Shanghai University)

  • Yuqi Zhang

    (Shanghai University)

  • Yueming Li

    (Shanghai University
    China United Network Communications Group Co., Ltd)

  • Zhaoxu Yu

    (East China University of Science and Technology)

Abstract

Online comments have become a valuable source for designers for the purpose of product improvement. However, the implicitly expressed users’ preferences on multiple product attributes in incomplete online comments make it difficult to extract useful information to improve the product from the online comments. In order to identify the users’ preference from the perspective of product improvement, the comment extension mining model is proposed to patch up the online reviews based on the semantic similarity and emotional resemblance. First of all, for the sake of mining the full context semantic information of the keywords in comments, they are transferred into vectors by the word2vec method. Next, smart semantic distance measurement models are developed to match the online comments with the standard comment templates that express users’ sentiment on product attributes based on the semantic similarity. Moreover, the fine-grained matching neural networks are designed to further match the reviews of each product attribute to its standard sentiment templates according to the user’s overall emotions towards the product. Finally, the KANO model is introduced to depict users’ preferences and develop product improvement strategies. The experiment on laptop product confirms that our method is effective.

Suggested Citation

  • Shugang Li & Yuqi Zhang & Yueming Li & Zhaoxu Yu, 2021. "The user preference identification for product improvement based on online comment patch," Electronic Commerce Research, Springer, vol. 21(2), pages 423-444, June.
    • Handle: RePEc:spr:elcore:v:21:y:2021:i:2:d:10.1007_s10660-019-09372-5
    DOI: 10.1007/s10660-019-09372-5
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    References listed on IDEAS

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    1. Nikolay Archak & Anindya Ghose & Panagiotis G. Ipeirotis, 2011. "Deriving the Pricing Power of Product Features by Mining Consumer Reviews," Management Science, INFORMS, vol. 57(8), pages 1485-1509, August.
    2. Decker, Reinhold & Trusov, Michael, 2010. "Estimating aggregate consumer preferences from online product reviews," International Journal of Research in Marketing, Elsevier, vol. 27(4), pages 293-307.
    3. Pradeep K. Chintagunta & Shyam Gopinath & Sriram Venkataraman, 2010. "The Effects of Online User Reviews on Movie Box Office Performance: Accounting for Sequential Rollout and Aggregation Across Local Markets," Marketing Science, INFORMS, vol. 29(5), pages 944-957, 09-10.
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    Cited by:

    1. Shugang Li & Fang Liu & Yuqi Zhang & Boyi Zhu & He Zhu & Zhaoxu Yu, 2022. "Text Mining of User-Generated Content (UGC) for Business Applications in E-Commerce: A Systematic Review," Mathematics, MDPI, vol. 10(19), pages 1-26, September.
    2. Yupeng Li & Yanan Dong & Yu Wang & Na Zhang, 2025. "Product design opportunity identification through mining the critical minority of customer online reviews," Electronic Commerce Research, Springer, vol. 25(1), pages 211-239, February.

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