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Text Mining of User-Generated Content (UGC) for Business Applications in E-Commerce: A Systematic Review

Author

Listed:
  • Shugang Li

    (School of Management, Shanghai University, Shanghai 200444, China)

  • Fang Liu

    (School of Management, Shanghai University, Shanghai 200444, China)

  • Yuqi Zhang

    (School of Management, Shanghai University, Shanghai 200444, China)

  • Boyi Zhu

    (School of Management, Shanghai University, Shanghai 200444, China)

  • He Zhu

    (School of Management, Shanghai University, Shanghai 200444, China)

  • Zhaoxu Yu

    (Department of Automation, East China University of Science and Technology, Shanghai 200237, China)

Abstract

In the Web2.0 era, user-generated content (UGC) provides a valuable source of data to aid in understanding consumers and driving intelligent business. Text mining techniques, such as semantic analysis and sentiment analysis, help to extract meaningful information embedded in UGC. However, research on text mining of UGC for e-commerce business applications involves interdisciplinary knowledge, and few studies have systematically summarized the research framework and application directions of related research in this field. First, based on e-commerce practice, in this study, we derive a general framework to summarize the mainstream research in this field. Second, widely used text mining techniques are introduced, including semantic and sentiment analysis. Furthermore, we analyze the development status of semantic analysis in terms of text representation and semantic understanding. Then, the definition, development, and technical classification of sentiment analysis techniques are introduced. Third, we discuss mainstream directions of text mining for business applications, ranging from high-quality UGC detection and consumer profiling, to product enhancement and marketing. Finally, research gaps with respect to these efforts are emphasized, and suggestions are provided for future work. We also provide prospective directions for future research.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jmathe:v:10:y:2022:i:19:p:3554-:d:928980
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    References listed on IDEAS

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    1. Wei Hong & Changyuan Zheng & Linhai Wu & Xujin Pu, 2019. "Analyzing the Relationship between Consumer Satisfaction and Fresh E-Commerce Logistics Service Using Text Mining Techniques," Sustainability, MDPI, vol. 11(13), pages 1-16, June.
    2. Chatterjee, Swagato & Goyal, Divesh & Prakash, Atul & Sharma, Jiwan, 2021. "Exploring healthcare/health-product ecommerce satisfaction: A text mining and machine learning application," Journal of Business Research, Elsevier, vol. 131(C), pages 815-825.
    3. Liu, Weihua & Long, Shangsong & Xie, Dong & Liang, Yanjie & Wang, Jinkun, 2021. "How to govern the big data discriminatory pricing behavior in the platform service supply chain?An examination with a three-party evolutionary game model," International Journal of Production Economics, Elsevier, vol. 231(C).
    4. Jian-Wu Bi & Yang Liu & Zhi-Ping Fan & Erik Cambria, 2019. "Modelling customer satisfaction from online reviews using ensemble neural network and effect-based Kano model," International Journal of Production Research, Taylor & Francis Journals, vol. 57(22), pages 7068-7088, November.
    5. Nilanshi Chauhan & Pardeep Singh, 2017. "Identifying the Opinion Orientation of Online Product Reviews at Feature Level: A Pruning Approach," International Journal of Information System Modeling and Design (IJISMD), IGI Global, vol. 8(2), pages 92-111, April.
    6. Sunil Saumya & Jyoti Prakash Singh, 2022. "Spam review detection using LSTM autoencoder: an unsupervised approach," Electronic Commerce Research, Springer, vol. 22(1), pages 113-133, March.
    7. Hui Yuan & Wei Xu & Qian Li & Raymond Lau, 2018. "Topic sentiment mining for sales performance prediction in e-commerce," Annals of Operations Research, Springer, vol. 270(1), pages 553-576, November.
    8. Nanne, Annemarie J. & Antheunis, Marjolijn L. & van der Lee, Chris G. & Postma, Eric O. & Wubben, Sander & van Noort, Guda, 2020. "The Use of Computer Vision to Analyze Brand-Related User Generated Image Content," Journal of Interactive Marketing, Elsevier, vol. 50(C), pages 156-167.
    9. Yue Ma & Guoqing Chen & Qiang Wei, 2017. "Finding users preferences from large-scale online reviews for personalized recommendation," Electronic Commerce Research, Springer, vol. 17(1), pages 3-29, March.
    10. Hui Zhang & Huguang Rao & Junzheng Feng, 2018. "Product innovation based on online review data mining: a case study of Huawei phones," Electronic Commerce Research, Springer, vol. 18(1), pages 3-22, March.
    11. Huosong Xia & Yitai Yang & Xiaoting Pan & Zuopeng Zhang & Wuyue An, 2020. "Sentiment analysis for online reviews using conditional random fields and support vector machines," Electronic Commerce Research, Springer, vol. 20(2), pages 343-360, June.
    12. Jianping Li & Yinhong Yao & Yuanjie Xu & Jingyu Li & Lu Wei & Xiaoqian Zhu, 2019. "Consumer’s risk perception on the Belt and Road countries: evidence from the cross-border e-commerce," Electronic Commerce Research, Springer, vol. 19(4), pages 823-840, December.
    13. 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.
    14. Donthu, Naveen & Kumar, Satish & Pandey, Neeraj & Pandey, Nitesh & Mishra, Akanksha, 2021. "Mapping the electronic word-of-mouth (eWOM) research: A systematic review and bibliometric analysis," Journal of Business Research, Elsevier, vol. 135(C), pages 758-773.
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