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Web celebrity shop assessment and improvement based on online review with probabilistic linguistic term sets by using sentiment analysis and fuzzy cognitive map

Author

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  • Decui Liang

    (University of Electronic Science and Technology of China)

  • Zhuoyin Dai

    (University of Electronic Science and Technology of China)

  • Mingwei Wang

    (University of Electronic Science and Technology of China)

  • Jinjun Li

    (Sichuan Tourism University)

Abstract

As a representative of the new economy, the web celebrity economy has achieved significant development in China with the rapid development of information technology and the Internet. In this environment, web celebrity shops encounter fierce business competition of peer competitors. Online reviews which imply the consumers’ attitudes and sentiments give the web celebrity shops good feedback to improve their competitiveness. Thus, taking milk tea as an example, this paper deeply investigates the assessment of web celebrity shops by mining online review. At the same time, we also discuss the competitive analysis and propose the corresponding improvement advices. In order to obtain the satisfaction assessments of web celebrity shops, on the one hand, we analyze topic extraction with latent dirichlet allocation (LDA) and determine the attributes that customers care about. On the other hand, we utilize long short-term memory (LSTM) and probabilistic linguistic term sets (PLTSs) to more precisely portray customers’ sentiment towards different attributes. By using fuzzy cognitive map (FCM) and the association rule, we further investigate the interrelationship among the attributes and construct the relationship graph between attributes for web celebrity shops. With the above results, we aggregate the decision information by designing improved extended Bonferroni mean (EBM) and obtain comprehensive evaluations. General speaking, this paper successfully transforms the unstructured data of online reviews into quantitative information and obtain satisfaction evaluations. With the aid of PLTSs and FCM, we further investigate the competitive analysis and propose improvement advices for each shop, which systematically provides us with a data-driven decision-making analysis model.

Suggested Citation

  • Decui Liang & Zhuoyin Dai & Mingwei Wang & Jinjun Li, 2020. "Web celebrity shop assessment and improvement based on online review with probabilistic linguistic term sets by using sentiment analysis and fuzzy cognitive map," Fuzzy Optimization and Decision Making, Springer, vol. 19(4), pages 561-586, December.
    • Handle: RePEc:spr:fuzodm:v:19:y:2020:i:4:d:10.1007_s10700-020-09327-8
    DOI: 10.1007/s10700-020-09327-8
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    References listed on IDEAS

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    1. Huchang Liao & Xiaomei Mi & Zeshui Xu, 2020. "A survey of decision-making methods with probabilistic linguistic information: bibliometrics, preliminaries, methodologies, applications and future directions," Fuzzy Optimization and Decision Making, Springer, vol. 19(1), pages 81-134, March.
    2. Wu, Xingli & Liao, Huchang, 2019. "A consensus-based probabilistic linguistic gained and lost dominance score method," European Journal of Operational Research, Elsevier, vol. 272(3), pages 1017-1027.
    3. Mingwei Lin & Zeshui Xu & Yuling Zhai & Zhiqiang Yao, 2018. "Multi-attribute group decision-making under probabilistic uncertain linguistic environment," Journal of the Operational Research Society, Taylor & Francis Journals, vol. 69(2), pages 157-170, February.
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    Cited by:

    1. Adjei Peter Darko & Decui Liang & Yinrunjie Zhang & Agbodah Kobina, 2023. "Service quality in football tourism: an evaluation model based on online reviews and data envelopment analysis with linguistic distribution assessments," Annals of Operations Research, Springer, vol. 325(1), pages 185-218, June.
    2. Wu, Xingli & Liao, Huchang, 2021. "Modeling personalized cognition of customers in online shopping," Omega, Elsevier, vol. 104(C).
    3. Xingli Wu & Huchang Liao, 2021. "Learning judgment benchmarks of customers from online reviews," OR Spectrum: Quantitative Approaches in Management, Springer;Gesellschaft für Operations Research e.V., vol. 43(4), pages 1125-1157, December.
    4. Love Offeibea Asiedu-Ayeh & Xungang Zheng & Kobina Agbodah & Bright Senyo Dogbe & Adjei Peter Darko, 2022. "Promoting the Adoption of Agricultural Green Production Technologies for Sustainable Farming: A Multi-Attribute Decision Analysis," Sustainability, MDPI, vol. 14(16), pages 1-21, August.
    5. Yinfeng Du & Zhen-Song Chen & Jie Yang & Juan Antonio Morente-Molinera & Lu Zhang & Enrique Herrera-Viedma, 2023. "A Textual Data-Oriented Method for Doctor Selection in Online Health Communities," Sustainability, MDPI, vol. 15(2), pages 1-19, January.
    6. Nazmiye Eligüzel, 2023. "Analyzing society anti-vaccination attitudes towards COVID-19: combining latent dirichlet allocation and fuzzy association rule mining with a fuzzy cognitive map," Fuzzy Optimization and Decision Making, Springer, vol. 22(4), pages 669-696, December.

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