A cross-platform market structure analysis method using online product reviews
Abstract
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. https://doi.org/10.3846/tede.2021.12005
This work is licensed under a Creative Commons Attribution 4.0 International License.
References
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. https://doi.org/10.3846/tede.2019.9383
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. https://doi.org/10.1016/j.elerap.2014.11.004
Chen, J. H., & Tsai, Y. C. (2020). Encoding candlesticks as images for pattern classification using convolutional neural networks. Financial Innovation, 6, 26. https://doi.org/10.1186/s40854-020-00187-0
Cooper, L. G., & Inoue, A. (1996). Building market structures from consumer preferences. Journal of Marketing Research, 33(3), 293–306. https://doi.org/10.1177/002224379603300304
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. https://doi.org/10.1023/A:1020222821774
Erdem, T. (1996). A dynamic analysis of market structure based on panel data. Marketing Science, 15(4), 359–378. https://doi.org/10.1287/mksc.15.4.359
Fazelabdolabadi, B. (2019). A hybrid Bayesian-network proposition for forecasting the crude oil price. Financial Innovation, 5(1), 30. https://doi.org/10.1186/s40854-019-0144-2
Ferri, C., Hernández-Orallo, J., & Modroiu, R. (2009). An experimental comparison of performance measures for classification. Pattern Recognition Letters, 30(1), 27–38. https://doi.org/10.1016/j.patrec.2008.08.010
Fraas, G. A., & Greer, F. D. (1977). Market structure and price collusion: An empirical analysis. The Journal of Industrial Economics, 26(1), 21–44. https://doi.org/10.2307/2098328
Fraser, C., & Bradford, J. W. (1983). Competitive market structure analysis: Principal partitioning of revealed substitutabilities. Journal of Consumer Research, 10(1), 15–29. https://doi.org/10.1086/208942
Galankashi, M. R., Rafiei, F. M., & Ghezelbash, M. (2020). Portfolio selection: a fuzzy-ANP approach. Financial Innovation, 6(1), 17. https://doi.org/10.1186/s40854-020-00175-4
Gupta, A., Dengre, V., Kheruwala, H. A., & Shah, M. (2020). Comprehensive review of text-mining applications in finance. Financial Innovation, 6(1), 39. https://doi.org/10.1186/s40854-020-00205-1
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. https://doi.org/10.1016/j.elerap.2010.04.001
Koksalmis, E., & Kabak, O. (2019). Deriving decision makers’ weights in group decision making: An overview of objective methods. Information Fusion, 49, 146–160. https://doi.org/10.1016/j.inffus.2018.11.009
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. https://doi.org/10.1186/s40854-019-0125-5
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. https://doi.org/10.3846/tede.2019.8740
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. https://doi.org/10.3846/20294913.2016.1210694
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. https://doi.org/10.1142/S0219622012500095
Kou, G., Peng, Y., & Wang, G. (2014). Evaluation of clustering algorithms for financial risk analysis using MCDM methods. Information Sciences, 275, 1–12. https://doi.org/10.1016/j.ins.2014.02.137
Lee, T., & Bradlow, E. (2011). Automated marketing research using online customer reviews. Social Science Electronic Publishing, 48(5), 881–894. https://doi.org/10.1509/jmkr.48.5.881
Li, G., Kou, G., & Peng, Y. (2016). A group decision making model for integrating heterogeneous information. IEEE Transactions on Systems, Man, and Cybernetics: Systems. https://doi.org/10.1109/TSMC.2016.2627050
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. https://doi.org/10.1109/TSMC.2017.2761360
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. https://doi.org/10.1007/s10479-020-03572-1
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. https://doi.org/10.1016/j.inffus.2016.11.012
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. https://doi.org/10.1142/S021962201750033X
Medhat, W., Hassan, A., & Korashy, H. (2014). Sentiment analysis algorithms and applications: A survey. Ain Shams Engineering Journal, 5(4), 1093–1113. https://doi.org/10.1016/j.asej.2014.04.011
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. https://doi.org/10.1007/s00607-015-0439-8
Netzer, O., Feldman, R., & Goldenberg, J., & Fresko, M. (2012). Mine your own business: Marketstructure surveillance through text mining. Marketing Science, 31(3), 521–543. https://doi.org/10.1287/mksc.1120.0713
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. https://doi.org/10.1007/s13369-014-1033-7
Šaparauskas, J., & Turskis, Z. (2006). Evaluation of construction sustainability by multiple criteria methods. Technological and Economic Development of Economy, 12(4), 321–326. https://doi.org/10.3846/13928619.2006.9637761
Schotten, P. C., & Morais, D. C. (2019). A group decision model for credit granting in the financial market. Financial Innovation, 5(1), 6. https://doi.org/10.1186/s40854-019-0126-4
Selvamuthu, D., Kumar, V., & Mishra, A. (2019). Indian stock market prediction using artificial neural networks on tick data. Financial Innovation, 5(1), 16. https://doi.org/10.1186/s40854-019-0131-7
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. https://doi.org/10.1177/002224298104500303
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. https://doi.org/10.1109/TSMC.2021.3049604
Yang, Q., Zhang, L., & Wang, X. (2017). Dynamic analysis on market structure of China’s coal industry. Energy Policy, 106, 498–504. https://doi.org/10.1016/j.enpol.2017.04.001
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. https://doi.org/10.1142/S0219622020500406
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. https://doi.org/10.1016/j.apm.2011.09.068
Zhang, J., Kou, G., Peng, Y., & Zhang, Y. (2021). Estimating priorities from relative deviations in pairwise comparison matrices. Information Sciences, 552, 310–327. https://doi.org/10.1016/j.ins.2020.12.008
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. https://doi.org/10.1016/j.ejor.2019.03.009
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. http://dl.acm.org/citation.cfm?id=1863201
Zhong, X., & Enke, D. (2019). Predicting the daily return direction of the stock market using hybrid machine learning algorithms. Financial Innovation, 5(1), 24. https://doi.org/10.1186/s40854-019-0138-0
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. https://doi.org/10.5755/j01.ee.24.5.4526
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. https://doi.org/10.1016/j.elerap.2014.11.004
Chen, J. H., & Tsai, Y. C. (2020). Encoding candlesticks as images for pattern classification using convolutional neural networks. Financial Innovation, 6, 26. https://doi.org/10.1186/s40854-020-00187-0
Cooper, L. G., & Inoue, A. (1996). Building market structures from consumer preferences. Journal of Marketing Research, 33(3), 293–306. https://doi.org/10.1177/002224379603300304
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. https://doi.org/10.1023/A:1020222821774
Erdem, T. (1996). A dynamic analysis of market structure based on panel data. Marketing Science, 15(4), 359–378. https://doi.org/10.1287/mksc.15.4.359
Fazelabdolabadi, B. (2019). A hybrid Bayesian-network proposition for forecasting the crude oil price. Financial Innovation, 5(1), 30. https://doi.org/10.1186/s40854-019-0144-2
Ferri, C., Hernández-Orallo, J., & Modroiu, R. (2009). An experimental comparison of performance measures for classification. Pattern Recognition Letters, 30(1), 27–38. https://doi.org/10.1016/j.patrec.2008.08.010
Fraas, G. A., & Greer, F. D. (1977). Market structure and price collusion: An empirical analysis. The Journal of Industrial Economics, 26(1), 21–44. https://doi.org/10.2307/2098328
Fraser, C., & Bradford, J. W. (1983). Competitive market structure analysis: Principal partitioning of revealed substitutabilities. Journal of Consumer Research, 10(1), 15–29. https://doi.org/10.1086/208942
Galankashi, M. R., Rafiei, F. M., & Ghezelbash, M. (2020). Portfolio selection: a fuzzy-ANP approach. Financial Innovation, 6(1), 17. https://doi.org/10.1186/s40854-020-00175-4
Gupta, A., Dengre, V., Kheruwala, H. A., & Shah, M. (2020). Comprehensive review of text-mining applications in finance. Financial Innovation, 6(1), 39. https://doi.org/10.1186/s40854-020-00205-1
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. https://doi.org/10.1016/j.elerap.2010.04.001
Koksalmis, E., & Kabak, O. (2019). Deriving decision makers’ weights in group decision making: An overview of objective methods. Information Fusion, 49, 146–160. https://doi.org/10.1016/j.inffus.2018.11.009
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. https://doi.org/10.1186/s40854-019-0125-5
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. https://doi.org/10.3846/tede.2019.8740
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. https://doi.org/10.3846/20294913.2016.1210694
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. https://doi.org/10.1142/S0219622012500095
Kou, G., Peng, Y., & Wang, G. (2014). Evaluation of clustering algorithms for financial risk analysis using MCDM methods. Information Sciences, 275, 1–12. https://doi.org/10.1016/j.ins.2014.02.137
Lee, T., & Bradlow, E. (2011). Automated marketing research using online customer reviews. Social Science Electronic Publishing, 48(5), 881–894. https://doi.org/10.1509/jmkr.48.5.881
Li, G., Kou, G., & Peng, Y. (2016). A group decision making model for integrating heterogeneous information. IEEE Transactions on Systems, Man, and Cybernetics: Systems. https://doi.org/10.1109/TSMC.2016.2627050
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. https://doi.org/10.1109/TSMC.2017.2761360
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. https://doi.org/10.1007/s10479-020-03572-1
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. https://doi.org/10.1016/j.inffus.2016.11.012
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. https://doi.org/10.1142/S021962201750033X
Medhat, W., Hassan, A., & Korashy, H. (2014). Sentiment analysis algorithms and applications: A survey. Ain Shams Engineering Journal, 5(4), 1093–1113. https://doi.org/10.1016/j.asej.2014.04.011
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. https://doi.org/10.1007/s00607-015-0439-8
Netzer, O., Feldman, R., & Goldenberg, J., & Fresko, M. (2012). Mine your own business: Marketstructure surveillance through text mining. Marketing Science, 31(3), 521–543. https://doi.org/10.1287/mksc.1120.0713
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. https://doi.org/10.1007/s13369-014-1033-7
Šaparauskas, J., & Turskis, Z. (2006). Evaluation of construction sustainability by multiple criteria methods. Technological and Economic Development of Economy, 12(4), 321–326. https://doi.org/10.3846/13928619.2006.9637761
Schotten, P. C., & Morais, D. C. (2019). A group decision model for credit granting in the financial market. Financial Innovation, 5(1), 6. https://doi.org/10.1186/s40854-019-0126-4
Selvamuthu, D., Kumar, V., & Mishra, A. (2019). Indian stock market prediction using artificial neural networks on tick data. Financial Innovation, 5(1), 16. https://doi.org/10.1186/s40854-019-0131-7
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. https://doi.org/10.1177/002224298104500303
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. https://doi.org/10.1109/TSMC.2021.3049604
Yang, Q., Zhang, L., & Wang, X. (2017). Dynamic analysis on market structure of China’s coal industry. Energy Policy, 106, 498–504. https://doi.org/10.1016/j.enpol.2017.04.001
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. https://doi.org/10.1142/S0219622020500406
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. https://doi.org/10.1016/j.apm.2011.09.068
Zhang, J., Kou, G., Peng, Y., & Zhang, Y. (2021). Estimating priorities from relative deviations in pairwise comparison matrices. Information Sciences, 552, 310–327. https://doi.org/10.1016/j.ins.2020.12.008
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. https://doi.org/10.1016/j.ejor.2019.03.009
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. http://dl.acm.org/citation.cfm?id=1863201
Zhong, X., & Enke, D. (2019). Predicting the daily return direction of the stock market using hybrid machine learning algorithms. Financial Innovation, 5(1), 24. https://doi.org/10.1186/s40854-019-0138-0
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. https://doi.org/10.5755/j01.ee.24.5.4526