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An integrated sentiment analysis and q-rung orthopair fuzzy MCDM model for supplier selection in E-commerce: a comprehensive approach

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Abstract

The process of selecting a supplier is a significant decision in supply chain management, as it can greatly impact the quality and cost of the procured products or services. This becomes even more important when shopping online, as there may be numerous options and thousands of reviews for a specific product type. In this research a novel hybrid methodology for supplier selection in e-commerce environment is introduced, which combines text mining and sentiment analysis of large customer review data and expert opinions of fuzzy multiple criteria decision-making (MCDM). Supplier selection requires expert perspective to determine the relevant criteria and assign them proper importance weights. Artificial intelligence is used to extract and interpret the emotional tone of customer reviews, adding valuable input to the determination of evaluation criteria and the rating of alternatives. The q-rung orthopair fuzzy set MCDM methodology, which is useful in situations with high levels of uncertainty or conflicting objectives and allows for the conversion of these qualitative expert opinions into a quantitative evaluation and determination of final criteria and their importance with the help of decision-makers' wisdom. By combining Artificial Intelligence techniques and MCDM approach, a more comprehensive and nuanced methodology to supplier selection is offered, taking into account both the qualitative and quantitative aspects of the decision. As two different real-life case studies, office chairs and robot vacuum cleaners from Amazon.com, both characterized by a substantial number of customer reviews and various features, were selected. Users' perspectives on multiple product features were identified, allowing for informed decisions and the provision of feedback on potential product improvements. Remarkably, the proposed methods aligned with the star ratings provided by 40,000 Amazon customers, underscoring the reliability and validity of the method. The proposed approach stands out in the supplier selection field with its innovative combination of sentiment analyses of customer review and perspectives of the decision experts, offering a cutting-edge tool for e-commerce managers to select or evaluate suppliers in e-commerce environment.

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Data availability

All the python code is shared in Github given in Appendix A. The data source that support the findings of this study are openly available in amazon.com website.

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  1. The School of Business, Shenandoah University, 1460 University Dr, Winchester, VA, 22601, United States

    Adem Pinar

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  1. Adem Pinar
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Correspondence to Adem Pinar.

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Appendix A

Appendix A

All the python code (includes Appendix A1, A2, and A3) can be accessed from https://github.com/apinar-thk/AI_Supplier_Selection/blob/main/Appendix-A.

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Pinar, A. An integrated sentiment analysis and q-rung orthopair fuzzy MCDM model for supplier selection in E-commerce: a comprehensive approach. Electron Commer Res (2023). https://doi.org/10.1007/s10660-023-09768-4

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