A cross-platform market structure analysis method using online product reviews

    Gang Kou Affiliation
    ; Pei Yang Affiliation
    ; Yi Peng Affiliation
    ; Hui Xiao Affiliation
    ; Feng Xiao Affiliation
    ; Yang Chen Affiliation
    ; Fawaz E. Alsaadi Affiliation


Studies have shown that online product reviews can indicate the position of a competitive brand. Even though reviews on different platforms may express different opinions, most studies are based on only one platform. This may lead to an inaccurate analysis of market structure. To solve this problem, we develop a novel market structure analysis based on multi-attribute group decision-making which can integrate reviews from different platforms. Multiple platforms more comprehensively reflect the market than single platforms do. To verify the effectiveness of the proposed method, we conduct a case study of mobile phone reviews across three top e-commerce platforms in China. In addition, we propose a process to generate priorities for product-attribute improvements using a cross-platform market structure analysis method. Our experiments demonstrate the effectiveness of the proposed method.

Keyword : market structure analysis, online product reviews, multi-attribute group decision making

How to Cite
Kou, G., Yang, P., Peng, Y., Xiao, H., Xiao, F., Chen, Y., & Alsaadi, F. E. (2021). A cross-platform market structure analysis method using online product reviews. Technological and Economic Development of Economy, 27(5), 992-1018.
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Aug 18, 2021
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This work is licensed under a Creative Commons Attribution 4.0 International License.


Chao, X., Kou, G., Peng, Y., & Alsaadi, F. E. (2019). Behavior monitoring methods for trade-based money laundering integrating macro and micro prudential regulation: A case from China. Technological and Economic Development of Economy, 25(6), 1081–1096.

Chen, K., Kou, G., & Shang, J. & Chen, Y. (2015). Visualizing market structure through online product reviews: Integrate topic modeling, TOPSIS, and multi-dimensional scaling approaches. Electronic Commerce Research and Applications, 14(1), 58–74.

Chen, J. H., & Tsai, Y. C. (2020). Encoding candlesticks as images for pattern classification using convolutional neural networks. Financial Innovation, 6, 26.

Cooper, L. G., & Inoue, A. (1996). Building market structures from consumer preferences. Journal of Marketing Research, 33(3), 293–306.

Elrod, T., Russell, G. J., Shocker, A. D., Andrews, R. L., Bacon, L., Bayus, B. L., Carroll, J. D., Johnson, R. M., Kamakura, W. A., Lenk, P., Mazanec, J. A., Rao, V. R., & Shankar, V. (2002). Inferring market structure from customer response to competing and complementary products. Marketing Letters, 13(3), 221–232.

Erdem, T. (1996). A dynamic analysis of market structure based on panel data. Marketing Science, 15(4), 359–378.

Fazelabdolabadi, B. (2019). A hybrid Bayesian-network proposition for forecasting the crude oil price. Financial Innovation, 5(1), 30.

Ferri, C., Hernández-Orallo, J., & Modroiu, R. (2009). An experimental comparison of performance measures for classification. Pattern Recognition Letters, 30(1), 27–38.

Fraas, G. A., & Greer, F. D. (1977). Market structure and price collusion: An empirical analysis. The Journal of Industrial Economics, 26(1), 21–44.

Fraser, C., & Bradford, J. W. (1983). Competitive market structure analysis: Principal partitioning of revealed substitutabilities. Journal of Consumer Research, 10(1), 15–29.

Galankashi, M. R., Rafiei, F. M., & Ghezelbash, M. (2020). Portfolio selection: a fuzzy-ANP approach. Financial Innovation, 6(1), 17.

Gupta, A., Dengre, V., Kheruwala, H. A., & Shah, M. (2020). Comprehensive review of text-mining applications in finance. Financial Innovation, 6(1), 39.

Koh, N. S., Hu, N., & Clemons, E. K. (2010). Do online reviews reflect a product’s true perceived quality? An investigation of online movie reviews across cultures. Electronic Commerce Research & Applications, 9(5), 374–385.

Koksalmis, E., & Kabak, O. (2019). Deriving decision makers’ weights in group decision making: An overview of objective methods. Information Fusion, 49, 146–160.

Kong, Y., Owusu-Akomeah, M., Antwi, H. A., Hu, X., & Acheampong, P. (2019). Evaluation of the robusticity of mutual fund performance in Ghana using Enhanced Resilient Backpropagation Neural Network (ERBPNN) and Fast Adaptive Neural Network Classifier (FANNC). Financial Innovation, 5(1), 10.

Kou, G., Chao, X., Peng, Y., Alsaadi, F. E., & Herrera-Viedma, E. (2019). Machine learning methods for systemic risk analysis in financial sectors. Technological and Economic Development of Economy, 25(5), 716–742.

Kou, G., Ergu, D., Chen, Y., & Lin, C. (2016). Pairwise comparison matrix in multiple criteria decision making. Technological and Economic Development of Economy, 22(5), 738–765.

Kou, G., Lu, Y., Peng, Y., & Shi, Y. (2012). Evaluation of classification algorithms using MCDM and rank correlation. International Journal of Information Technology & Decision Making, 11(01), 197–225.

Kou, G., Peng, Y., & Wang, G. (2014). Evaluation of clustering algorithms for financial risk analysis using MCDM methods. Information Sciences, 275, 1–12.

Lee, T., & Bradlow, E. (2011). Automated marketing research using online customer reviews. Social Science Electronic Publishing, 48(5), 881–894.

Li, G., Kou, G., & Peng, Y. (2016). A group decision making model for integrating heterogeneous information. IEEE Transactions on Systems, Man, and Cybernetics: Systems.

Li, T., Kou, G., Peng, Y., & Shi, Y. (2017). Classifying with adaptive hyper-spheres: An incremental classifier based on competitive learning. IEEE Transactions on Systems, Man, and Cybernetics: Systems.

Lin, C., Kou, G., Peng, Y., & Alsaadi, F. E. (2020). Aggregation of the nearest consistency matrices with the acceptable consensus in AHP-GDM. Annals of Operations Research.

Liu, Y., Bi, J. W., & Fan, Z. P. (2017b). Ranking products through online reviews: A method based on sentiment analysis technique and intuitionistic fuzzy set theory. Information Fusion, 36, 149–161.

Liu, Y., Bi, J. W., & Fan, Z. (2017a). 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, 16(6), 1497–1522.

Medhat, W., Hassan, A., & Korashy, H. (2014). Sentiment analysis algorithms and applications: A survey. Ain Shams Engineering Journal, 5(4), 1093–1113.

Najmi, E., Hashmi, K., Malik, Z., Rezgui, A., & Khan, H. U. (2015). CAPRA: a comprehensive approach to product ranking using customer reviews. Computing, 97(8), 843–867.

Netzer, O., Feldman, R., & Goldenberg, J., & Fresko, M. (2012). Mine your own business: Marketstructure surveillance through text mining. Marketing Science, 31(3), 521–543.

Peng, Y., Kou, G., & Li, J. (2014). A fuzzy PROMETHEE approach for mining customer reviews in Chinese. Arabian Journal for Science and Engineering, 39(6), 5245–5252.

Šaparauskas, J., & Turskis, Z. (2006). Evaluation of construction sustainability by multiple criteria methods. Technological and Economic Development of Economy, 12(4), 321–326.

Schotten, P. C., & Morais, D. C. (2019). A group decision model for credit granting in the financial market. Financial Innovation, 5(1), 6.

Selvamuthu, D., Kumar, V., & Mishra, A. (2019). Indian stock market prediction using artificial neural networks on tick data. Financial Innovation, 5(1), 16.

Srivastava, R. K., Leone, R. P., & Shocker, A. D. (1981). Market structure analysis: Hierarchical clustering of products based on substitution-in-use. Journal of Marketing, 45(3), 38–48.

Wang, H., Kou, G., & Peng, Y. (2021). An iterative algorithm to derive priority from large-scale sparse pairwise comparison matrix. IEEE Transactions on Systems, Man, and Cybernetics: Systems.

Yang, Q., Zhang, L., & Wang, X. (2017). Dynamic analysis on market structure of China’s coal industry. Energy Policy, 106, 498–504.

Yu, D., Kou, G., Xu, Z., & Shi, S. (2021). Analysis of collaboration evolution in AHP research: 1982–2018. International Journal of Information Technology & Decision Making (IJITDM), 20(1), 7–36.

Yue, Z. (2012). Approach to group decision making based on determining the weights of experts by using projection method. Applied Mathematical Modelling, 36(7), 2900–2910.

Zhang, J., Kou, G., Peng, Y., & Zhang, Y. (2021). Estimating priorities from relative deviations in pairwise comparison matrices. Information Sciences, 552, 310–327.

Zhang, H., Kou, G., & Peng, Y. (2019). Soft consensus cost models for group decision making and economic interpretations. European Journal of Operational Research, 277(3), 964–980.

Zhang, K., Narayanan, R., & Choudhary, A. (2010). Voice of the customers: mining online customer reviews for product feature-based ranking. In Proceedings of the 3rd Conference on Online Social Networks.

Zhong, X., & Enke, D. (2019). Predicting the daily return direction of the stock market using hybrid machine learning algorithms. Financial Innovation, 5(1), 24.

Zolfani, S. H., & Saparauskas, J. (2013). New application of SWARA method in prioritizing sustainability assessment indicators of energy system. Engineering Economics, 24(5), 408–414.