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A Method for Ranking Products Through Online Reviews Based on Sentiment Classification and Interval-Valued Intuitionistic Fuzzy TOPSIS

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  • Yang Liu

    (Department of Information Management and Decision Sciences, School of Business Administration, Northeastern University, Shenyang 110167, P. R. China)

  • Jian-Wu Bi

    (Department of Information Management and Decision Sciences, School of Business Administration, Northeastern University, Shenyang 110167, P. R. China)

  • Zhi-Ping Fan

    (Department of Information Management and Decision Sciences, School of Business Administration, Northeastern University, Shenyang 110167, P. R. China†State Key Laboratory of Synthetical Automation for Process Industries, Northeastern University, Shenyang 110819, P. R. China)

Abstract

Studies have shown that online product reviews significantly affect consumer purchase decisions. However, it is difficult for the consumer to read online product reviews one by one because the number of online reviews is very large. Thus, to facilitate consumer purchase decisions, how to rank products through online reviews is a valuable research topic. This paper proposes a method for ranking products through online reviews based on sentiment classification and the interval-valued intuitionistic fuzzy Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS). The method consists of two parts: (1) identifying sentiment orientations of the online reviews based on sentiment classification and (2) ranking alternative products based on interval-valued intuitionistic fuzzy TOPSIS. In the first part, the online reviews of the alternative products concerning multiple attributes are preprocessed, and an algorithm based on support vector machine and one-versus-one strategy is developed for classifying the sentiment orientations of online reviews into three categories: positive, neutral, and negative. In the second part, based on the percentages of the online reviews with different sentiment orientations and the numbers of online reviews of different products crawled from the website, an interval-valued intuitionistic fuzzy number is constructed to represent the performance of an alternative product with respect to the product attribute. Additionally, the interval-valued intuitionistic fuzzy TOPSIS method is employed to determine a ranking of the alternative products. Finally, a case analysis is provided to illustrate the application of the proposed method.

Suggested Citation

  • Yang Liu & Jian-Wu Bi & Zhi-Ping Fan, 2017. "A Method for Ranking Products Through Online Reviews Based on Sentiment Classification and Interval-Valued Intuitionistic Fuzzy TOPSIS," International Journal of Information Technology & Decision Making (IJITDM), World Scientific Publishing Co. Pte. Ltd., vol. 16(06), pages 1497-1522, November.
    • Handle: RePEc:wsi:ijitdm:v:16:y:2017:i:06:n:s021962201750033x
    DOI: 10.1142/S021962201750033X
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    References listed on IDEAS

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    1. Yijun Li & Qiang Ye & Ziqiong Zhang & Tienan Wang, 2011. "Snippet-Based Unsupervised Approach For Sentiment Classification Of Chinese Online Reviews," International Journal of Information Technology & Decision Making (IJITDM), World Scientific Publishing Co. Pte. Ltd., vol. 10(06), pages 1097-1110.
    2. Gang Kou & Yanqun Lu & Yi Peng & Yong Shi, 2012. "Evaluation Of Classification Algorithms Using Mcdm And Rank Correlation," International Journal of Information Technology & Decision Making (IJITDM), World Scientific Publishing Co. Pte. Ltd., vol. 11(01), pages 197-225.
    3. Zeshui Xu & Hui Hu, 2010. "Projection Models For Intuitionistic Fuzzy Multiple Attribute Decision Making," International Journal of Information Technology & Decision Making (IJITDM), World Scientific Publishing Co. Pte. Ltd., vol. 9(02), pages 267-280.
    4. Kou, Gang & Lin, Changsheng, 2014. "A cosine maximization method for the priority vector derivation in AHP," European Journal of Operational Research, Elsevier, vol. 235(1), pages 225-232.
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    Citations

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    Cited by:

    1. Meng Zhao & Xinyuan Shen & Huchang Liao & Mingyao Cai, 2022. "Selecting products through text reviews: An MCDM method incorporating personalized heuristic judgments in the prospect theory," Fuzzy Optimization and Decision Making, Springer, vol. 21(1), pages 21-44, March.
    2. Ayat Zaki Ahmed & Manuel Rodríguez-Díaz, 2020. "Significant Labels in Sentiment Analysis of Online Customer Reviews of Airlines," Sustainability, MDPI, vol. 12(20), pages 1-18, October.
    3. Heidary Dahooie, Jalil & Raafat, Romina & Qorbani, Ali Reza & Daim, Tugrul, 2021. "An intuitionistic fuzzy data-driven product ranking model using sentiment analysis and multi-criteria decision-making," Technological Forecasting and Social Change, Elsevier, vol. 173(C).
    4. Melda Kokoç & Süleyman Ersöz, 2021. "A literature review of interval-valued intuitionistic fuzzy multi-criteria decision-making methodologies," Operations Research and Decisions, Wroclaw University of Science and Technology, Faculty of Management, vol. 31(4), pages 89-116.
    5. Paola Zola & Paulo Cortez & Costantino Ragno & Eugenio Brentari, 2019. "Social Media Cross-Source and Cross-Domain Sentiment Classification," International Journal of Information Technology & Decision Making (IJITDM), World Scientific Publishing Co. Pte. Ltd., vol. 18(05), pages 1469-1499, September.
    6. Jerzy Michnik & Artur Grabowski, 2020. "Modeling Uncertainty in the Wings Method Using Interval Arithmetic," International Journal of Information Technology & Decision Making (IJITDM), World Scientific Publishing Co. Pte. Ltd., vol. 19(01), pages 221-240, January.
    7. Lifeng He & Dongmei Han & Xiaohang Zhou & Zheng Qu, 2020. "The Voice of Drug Consumers: Online Textual Review Analysis Using Structural Topic Model," IJERPH, MDPI, vol. 17(10), pages 1-18, May.

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