IDEAS home Printed from https://ideas.repec.org/a/taf/tprsxx/v57y2019i22p7068-7088.html
   My bibliography  Save this article

Modelling customer satisfaction from online reviews using ensemble neural network and effect-based Kano model

Author

Listed:
  • Jian-Wu Bi
  • Yang Liu
  • Zhi-Ping Fan
  • Erik Cambria

Abstract

With the rapid advances in information technology, an increasing number of online reviews are posted daily on the Internet. Such reviews can serve as a promising data source to understand customer satisfaction. To this end, in this paper, we proposed a method for modelling customer satisfaction from online reviews. In the method, customer satisfaction dimensions (CSDs) are first extracted from online reviews based on latent dirichlet allocation (LDA). The sentiment orientations of the extracted CSDs are identified using a support vector machine (SVM). Then, considering the existence of complex relationships among different CSDs and the customer satisfaction, an ensemble neural network based model (ENNM) is proposed to measure the effects of customer sentiments toward different CSDs on customer satisfaction. On this basis, to identify the category of each CSD from the customer’s perspective, an effect-based Kano model (EKM) is proposed. Finally, an empirical study, which consists of two parts (phones and cameras), is given to illustrate the effectiveness of the proposed method.

Suggested Citation

  • 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.
    • Handle: RePEc:taf:tprsxx:v:57:y:2019:i:22:p:7068-7088
    DOI: 10.1080/00207543.2019.1574989
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1080/00207543.2019.1574989
    Download Restriction: Access to full text is restricted to subscribers.
    File URL: https://libkey.io/10.1080/00207543.2019.1574989?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Wang, Binni & Wang, Pong & Tu, Yiliu, 2021. "Customer satisfaction service match and service quality-based blockchain cloud manufacturing," International Journal of Production Economics, Elsevier, vol. 240(C).
    2. Junegak Joung & Ki-Hun Kim & Kwangsoo Kim, 2021. "Data-Driven Approach to Dual Service Failure Monitoring From Negative Online Reviews: Managerial Perspective," SAGE Open, , vol. 11(1), pages 21582440209, January.
    3. Nasiri, Mohammad Sadegh & Shokouhyar, Sajjad, 2021. "Actual consumers' response to purchase refurbished smartphones: Exploring perceived value from product reviews in online retailing," Journal of Retailing and Consumer Services, Elsevier, vol. 62(C).
    4. Zihayat, Morteza & Ayanso, Anteneh & Davoudi, Heidar & Kargar, Mehdi & Mengesha, Nigussie, 2021. "Leveraging non-respondent data in customer satisfaction modeling," Journal of Business Research, Elsevier, vol. 135(C), pages 112-126.
    5. Zhang, Dianfeng & Shen, Zifan & Li, Yanlai, 2023. "Requirement analysis and service optimization of multiple category fresh products in online retailing using importance-Kano analysis," Journal of Retailing and Consumer Services, Elsevier, vol. 72(C).
    6. Maria Rostasova & Anna Padourova & Tatiana Corejova, 2020. "KANO model as a tool of effective customer satisfaction diagnostics of postal services," Entrepreneurship and Sustainability Issues, VsI Entrepreneurship and Sustainability Center, vol. 8(2), pages 811-828, December.
    7. 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.
    8. Wu, Jie & Zhao, Narisa & Yang, Tong, 2024. "Wisdom of crowds: SWOT analysis based on hybrid text mining methods using online reviews," Journal of Business Research, Elsevier, vol. 171(C).
    9. Yuan Yuan & Tianhui You & Tian’ai Xu & Xun Yu, 2022. "Customer-Oriented Strategic Planning for Hotel Competitiveness Improvement Based on Online Reviews," Sustainability, MDPI, vol. 14(22), pages 1-30, November.
    10. Zibarzani, Masoumeh & Abumalloh, Rabab Ali & Nilashi, Mehrbakhsh & Samad, Sarminah & Alghamdi, O.A. & Nayer, Fatima Khan & Ismail, Muhammed Yousoof & Mohd, Saidatulakmal & Mohammed Akib, Noor Adelyna, 2022. "Customer satisfaction with Restaurants Service Quality during COVID-19 outbreak: A two-stage methodology," Technology in Society, Elsevier, vol. 70(C).
    11. Qiuying Chen & Shangyue Xu & Ronghui Liu & Qingquan Jiang, 2023. "Exploring the Discrepancy between Projected and Perceived Destination Images: A Cross-Cultural and Sustainable Analysis Using LDA Modeling," Sustainability, MDPI, vol. 15(12), pages 1-31, June.
    12. Ming-Tsang Lu & Hsi-Peng Lu & Chiao-Shan Chen, 2022. "Exploring the Key Priority Development Projects of Smart Transportation for Sustainability: Using Kano Model," Sustainability, MDPI, vol. 14(15), pages 1-19, July.
    13. Lionel Nicod & Élodie Mallor & Sylvie Llosa, 2023. "L’influence de l’aide à participer en magasin sur la satisfaction client : une approche par le modèle tétraclasse," Post-Print hal-04311121, HAL.
    14. Yanlai Li & Zifan Shen & Cuiming Zhao & Kwai-Sang Chin & Xuwei Lang, 2024. "Understanding Customer Opinion Change on Fresh Food E-Commerce Products and Services—Comparative Analysis before and during COVID-19 Pandemic," Sustainability, MDPI, vol. 16(7), pages 1-22, March.
    15. Zhang, Min & Sun, Lin & Wang, G. Alan & Li, Yuzhuo & He, Shuguang, 2022. "Using neutral sentiment reviews to improve customer requirement identification and product design strategies," International Journal of Production Economics, Elsevier, vol. 254(C).
    16. Zhang, Chenxi & Xu, Zeshui, 2024. "Gaining insights for service improvement through unstructured text from online reviews," Journal of Retailing and Consumer Services, Elsevier, vol. 80(C).
    17. Jia-Li Chang & Hui Li & Jian Wu, 2023. "How Tourist Group Books Hotels Meeting the Majority Affective Expectations: A Group Selection Frame with Kansei Text Mining and Consensus Coordinating," Group Decision and Negotiation, Springer, vol. 32(2), pages 327-358, April.
    18. Pingping Cao & Jin Zheng & Mingyang Li, 2023. "Product Selection Considering Multiple Consumers’ Expectations and Online Reviews: A Method Based on Intuitionistic Fuzzy Soft Sets and TODIM," Mathematics, MDPI, vol. 11(17), pages 1-20, September.
    19. Wen-Kuo Chen & Dalianus Riantama & Long-Sheng Chen, 2020. "Using a Text Mining Approach to Hear Voices of Customers from Social Media toward the Fast-Food Restaurant Industry," Sustainability, MDPI, vol. 13(1), pages 1-17, December.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:taf:tprsxx:v:57:y:2019:i:22:p:7068-7088. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Chris Longhurst (email available below). General contact details of provider: http://www.tandfonline.com/TPRS20 .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.