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Research Article

Grey-Markov model of user demands prediction based on online reviews

, , , &
Pages 487-521 | Received 24 Feb 2023, Accepted 01 Jul 2023, Published online: 13 Jul 2023

References

  • Ahmad, W., H. U. Khan, T. Iqbal, and S. Iqbal. 2023. “Attention-based Multi-channel Gated Recurrent Neural Networks: A Novel Feature-Centric Approach for Aspect-Based Sentiment Classification.” IEEE Access 11:54408–54427. https://doi.org/10.1109/ACCESS.2023.3281889.
  • Alzate, M., M. Arce-Urriza, and J. Cebollada. 2022. “Mining the Text of Online Consumer Reviews to Analyze Brand Image and Brand Positioning.” Journal of Retailing and Consumer Services 67:102989. https://doi.org/10.1016/j.jretconser.2022.102989.
  • Anh, K. Q., Y. Nagai, and N. L. Minh. 2019. “Extracting User Requirements from Online Reviews for Product Design: A Supportive Framework for Designers.” Journal of Intelligent & Fuzzy Systems 37 (6): 7441–7451. https://doi.org/10.3233/JIFS-179352.
  • Blei, D. M., K. Franks, M. I. Jordan, and I. S. Mian. 2006. “Statistical Modeling of Biomedical Corpora: Mining the Caenorhabditis Genetic Center Bibliography for Genes Related to Life Span.” BMC Bioinformatics 7 (1): 250. https://doi.org/10.1186/1471-2105-7-250.
  • Chan, K. Y., T. S. Dillon, C. K. Kwong, and S. H. Ling. 2011. “Using Genetic Programming for Developing Relationship Between Engineering Characteristics and Customer Requirements in New Products.” 6th IEEE Conference on Industrial Electronics and Applications 2011:526–531.
  • Chang, D. N., and C. Lee. 2018. “A Product Affective Properties Identification Approach Based on web Mining in a Crowdsourcing Environment.” Journal of Engineering Design 29 (8-9): 449–483. https://doi.org/10.1080/09544828.2018.1463514.
  • Chatterjee, P. 2001. “Online Review: Do Consumers Use Them?” Advances in Consumer Research 15 (28): 133–139.
  • Chen, Y. 2020. “Mining of Instant Messaging Data in the Internet of Things Based on Support Vector Machine.” Computer Communications 154:278–287. https://doi.org/10.1016/j.comcom.2020.02.080.
  • Chen, F., and Y. F. Huang. 2019. “Knowledge-enhanced Neural Networks for Sentiment Analysis of Chinese Reviews.” Neurocomputing 368:51–58. https://doi.org/10.1016/j.neucom.2019.08.054.
  • Chen, K. J., J. Jin, and J. Y. Luo. 2022. “Big Consumer Opinion Data Understanding for Kano Categorization in New Product Development.” Journal of Ambient Intelligent and Humanized Computing 13 (4): 2269–2288. https://doi.org/10.1007/s12652-021-02985-5.
  • Chen, Z. J., Y. C. Zhu, Y. Q. Di, and S. C. Feng. 2015. “Self-adaptive Prediction of Cloud Resource Demands Using Ensemble Model and Subtractive-Fuzzy Clustering Based Fuzzy Neural Network.” Computational Intelligence and Neuroscience 2015:919805. https://doi.org/10.1155/2015/919805.
  • Cheng, F. M., S. H. Yu, J. J. Chu, J. S. Fan, and Y. K. Hu. 2022. “Customer Satisfaction-Oriented Product Configuration Approach Based on Online Product Reviews.” Multimedia Tools and Applications 81 (3): 4413–4433. https://doi.org/10.1007/s11042-021-11774-3.
  • Choi, T. M., C. L. Hui, N. Liu, S. F. Ng, and Y. Yu. 2014. “Fast Fashion Sales Forecasting with Limited Data and Time.” Decision Support Systems 59:84–92. https://doi.org/10.1016/j.dss.2013.10.008.
  • Chong, A. Y. L., B. Y. Li, E. W. T. Ngai, E. Ch'ng, and F. Lee. 2016. “Predicting Online Product Sales via Online Reviews, Sentiments, and Promotion Strategies A Big Data Architecture and Neural Network Approach.” International Journal of Operations & Production Management 36 (4): 358-+. https://doi.org/10.1108/IJOPM-03-2015-0151.
  • Cui, Z. M., Q. L. Qiu, C. Yin, J. Yu, Z. D. Wu, and A. Y. Deng. 2019. “A Barrage Sentiment Analysis Scheme Based on Expression and Tone.” IEEE Access 7:180324–180335. https://doi.org/10.1109/ACCESS.2019.2957279.
  • Dou, R. L., W. Li, and G. F. Nan. 2019. “An Integrated Approach for Dynamic Customer Requirement Identification for Product Development.” Enterprise Information Systems 13 (4): 448–466. https://doi.org/10.1080/17517575.2018.1526321.
  • Duan, Y., T. Liu, and Z. Mao. 2022. “How Online Reviews and Coupons Affect Sales and Pricing: An Empirical Study Based on e-Commerce Platform.” Journal of Retailing and Consumer Services 65:102846. https://doi.org/10.1016/j.jretconser.2021.102846.
  • Gupta, R. K., and K. Kaushik. 2022. “Linking Text Characteristics of Ideas to Their Popularity in Online User Innovation Communities.” Computers in Human Behavior 136:107382. https://doi.org/10.1016/j.chb.2022.107382.
  • Hao, J., X. Q. Gao, Y. Liu, and Z. P. Han. 2023. “Acquisition Method of User Requirements for Complex Products Based on Data Mining.” Sustainability 15 (9): 7566. https://doi.org/10.3390/su15097566.
  • Hu, Y. H., K. C. Chen, and P. J. Lee. 2017. “The Effect of User-Controllable Filters on the Prediction of Online Hotel Reviews.” Information & Management 54 (6): 728–744. https://doi.org/10.1016/j.im.2016.12.009.
  • Hu, M. Q., and B. Liu. 2004. “Mining Opinion Features in Customer Reviews.” Proceeding of the Nineteenth National Conference on Artificial Intelligence and the Sixteenth Conference on Innovative Applications of Artificial Intelligence 4 (4): 755–760.
  • Huang, A. H., H. B. Pu, W. G. Li, and G. Q. Ye. 2012. “Forecast of Importance Weights of Customer Requirements Based on Artificial Immune System and Least Square Support Vector Machine.” Proceedings of 19th Annual International Conference on Management Science and Engineering 83–88.
  • Jeong, H., D. Shin, and J. Choi. 2011. “FEROM: Feature Extraction and Refinement for Opinion Mining.” ETRI Journal 33 (5): 720–730. https://doi.org/10.4218/etrij.11.0110.0627.
  • Jiang, H., C. K. Kwong, G. E. O. Kremer, and W. Y. Park. 2019. “Dynamic Modelling of Customer Preferences for Product Design Using DENFIS and Opinion Mining.” Advanced Engineering Informatics 42:100969. https://doi.org/10.1016/j.aei.2019.100969.
  • Jiang, H. M., C. K. Kwong, W. Y. Park, and K. M. Yu. 2018. “A Multi-objective PSO Approach of Mining Association Rules for Affective Design Based on Online Customer Reviews.” Journal of Engineering Design 29 (7): 381–403. https://doi.org/10.1080/09544828.2018.1475629.
  • Jiang, H. M., C. K. Kwong, and K. L. Yung. 2017. “Predicting Future Importance of Product Features Based on Online Custom er Reviews.” Journal of Mechanical Design 139 (11): 111413. https://doi.org/10.1115/1.4037348.
  • Jiang, H. M., F. Sabetzadeh, and C. K. Kwong. 2021. “Dynamic Analysis of Customer Needs Using Opinion Mining and Fuzzy Time Series Approaches.” IEEE International Conference on Fuzzy Systems 2021. https://doi.org/10.1109/FUZZ45933.2021.9494527.
  • Jiang, X., Y. Wang, and J. P. Chai. 2015. “Research of Users’ VOD Viewing Behaviour Based on ARMA Model.” Proceedings of the AASRI International Conference on Industrial Electronics and Applications 2015 (2): 272–274.
  • Jiao, Y., and Y. Yang. 2019. “A Product Configuration Approach Based on Online Data.” Journal of Intelligent Manufacturing 30 (6): 2473–2487. https://doi.org/10.1007/s10845-018-1406-y.
  • Jin, J., P. Ji, and Y. Liu. 2014. “Prioritising Engineering Characteristics Based on Customer Online Reviews for Quality Function Deployment.” Journal of Engineering Design 25 (7-9): 303–324. https://doi.org/10.1080/09544828.2014.984665.
  • Jin, J., P. Ji, and S. X. Yan. 2019. “Comparison of Series Products from Customer Online Concerns for Competitive Intelligence.” Journal of Ambient Intelligent and Humanized Computing 10 (3): 937–952. https://doi.org/10.1007/s12652-017-0635-9.
  • Kauffmann, E., J. Peral, D. Gil, A. Ferrandez, R. Sellers, and H. Mora. 2019. “Managing Marketing Decision-Making with Sentiment Analysis: An Evaluation of the Main Product Features Using Text Data Mining.” Sustainability 11 (15): 4235. https://doi.org/10.3390/su11154235.
  • Kazmaier, J., and J. H. van Vuuren. 2020. “A Generic Framework for Sentiment Analysis: Leveraging Opinion-Bearing Data to Inform Decision Making.” Decision Support Systems 135:113304. https://doi.org/10.1016/j.dss.2020.113304.
  • Khalili, M., K. Ta, J. F. Boorisoff, and H. F. M. Van der Loos. 2021. “Offline and Real-Time Implementation of a Personalized Wheelchair User Intention Detection Pipeline: A Case Study.” Proceedings of the 30th IEEE International Conference on Robot and Human Interactive Communication 1210–1215. https://doi.org/10.1109/RO-MAN50785.2021.9515488.
  • Kim, J., Y. Lee, and I. Song. 2021. “From Intuition to Intelligence: A Text Mining-Based Approach for Movies’ Green-Lighting Process.” Internet Research 32 (3): 1003–1022. https://doi.org/10.1108/INTR-11-2020-0651.
  • Kim, H. S., and Y. Noh. 2019. “Elicitation of Design Factors Through big Data Analysis of Online Customer Reviews for Washing Machines.” Journal of Mechanical Science and Technology 33 (6): 2785–2795. https://doi.org/10.1007/s12206-019-0525-5.
  • Lee, H. C., H. C. Rim, and D. G. Lee. 2019. “Learning to Rank Products Based on Online Product Reviews Using a Hierarchical Deep Neural Network.” Electronic Commerce Research and Applications 36:100874. https://doi.org/10.1016/j.elerap.2019.100874.
  • Lee, C., X. Xu, and C. C. Lin. 2019. “Using Online User-Generated Reviews to Predict Offline Box-Office Sales and Online DVD Store Sales in the O2O Era.” Journal of Theoretical and Applied Electronic Commerce Research 14 (1): 68–83. https://doi.org/10.4067/S0718-1876201900010010.
  • Li, X., J. N. Su, Z. P. Zhang, and R. S. Bai. 2021a. “Product Innovation Concept Generation Based on Deep Learning and Kansei Engineering.” Journal of Engineering Design 32 (10): 559–589. https://doi.org/10.1080/09544828.2021.1928023.
  • Li, X., X. Y. Wang, W. Shao, L. Y. Xia, G. S. Zhang, B. Tian, W. D. Li, and P. Peng. 2007. “Forecast of Flood in Chaohu Lake Basin of China Based on Grey-Markov Theory.” Chinese Geographical Science 17 (1): 64–68. https://doi.org/10.1007/s11769-007-0064-3.
  • Li, J. Q., Q. Q. Wang, Y. T. Xuan, and H. Zhou. 2021b. “User Demands Analysis of Eco-City Based on the Kano Model – An Application to China Case Study.” PLoS one 16 (3): e0248187–e0248187. https://doi.org/10.1371/journal.pone.0248187.
  • Li, M., and J. Zhang. 2021. “Integrating Kano Model, AHP, and QFD Methods for New Product Development Based on Text Mining, Intuitionistic Fuzzy Sets, and Customers Satisfaction.” Mathematical Problems in Engineering 17 (10): 3648. https://doi.org/10.1155/2021/2349716.
  • Li, M. F., G. X. Zhang, L. T. Zhao, and T. Song. 2022. “Extracting Product Competitiveness Through User-Generated Content: A Hybrid Probabilistic Inference Model.” Computer and Information Sciences 34 (6): 2720–2732. https://doi.org/10.1016/j.jksuci.2022.03.018.
  • Liu, J. F., and L. Gao. 2021. “Analysis of Topics and Characteristics of User Reviews on Different Online Psychological Counseling Methods.” International Journal of Medical Informatics 147:104367. https://doi.org/10.1016/j.ijmedinf.2020.104367.
  • Liu, H. J., K. X. Xu, and Z. H. Pan. 2016. “Forecast Method of Customer Needs Volatility to Personalized Product.” Proceedings of the 11th ASME International Manufacturing Science and Engineering Conference 2016(2): UNSP V002T04A023.
  • Liu, X., and S. X. Yang. 2022. “Study on Product Form Design via Kansei Engineering and Virtual Reality.” Journal of Engineering Design 33 (6): 412–440. https://doi.org/10.1080/09544828.2022.2078660.
  • Liu, X., S. X. Yang, and Y. X. Wu. 2023. “Product Emotional Design Method Based on Image Metaphor: A Technical Note.” Journal of Engineering Design 34 (2): 180–201. https://doi.org/10.1080/09544828.2023.2179276.
  • Long, C., J. X. Zhang, and X. Zhu. 2010. “A Review Selection Approach for Accurate Feature Rating Estimation.” Proceedings of 23rd International Conference on Computational Linguistics 766–774.
  • Lucini, F. R., L. M. Tonetto, F. S. Fogliatto, and M. J. Anzanello. 2020. “Text Mining Approach to Explore Dimensions of Airline Customer Satisfaction Using Online Customer Reviews.” Journal of Air Transport Management 83:101760. https://doi.org/10.1016/j.jairtraman.2019.101760.
  • Luo, J. M., H. Q. Vu, G. Li, and R. Law. 2021. “Understanding Service Attributes of Robot Hotels: A Sentiment Analysis of Customer Online Reviews.” International Journal of Hospitality Management 98:103032. https://doi.org/10.1016/j.ijhm.2021.103032.
  • Ostasz, G., D. Siwiec, and A. Pacana. 2022. “Universal Model to Predict Expected Direction of Products Quality Improvement.” Energies 15 (5): 1751. https://doi.org/10.3390/en15051751.
  • Pimpinella, A., A. E. C. Redondi, I. Galimberti, F. Foglia, and L. Venturini. 2019. “Towards Long-Term Coverage and Video Users Satisfaction Prediction in Cellular Networks.” Proceedings of the 2019 12th IFIP Wireless and Mobile Networking Conference 146–153.
  • Ping, Y. K., C. Gao, T. C. Liu, X. Y. Du, H. L. Luo, D. P. Jin, and Y. Li. 2021. “User Consumption Intention Prediction in Meituan.” Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery and Data Mining 3472–3482. https://doi.org/10.1145/3447548.3467178.
  • Rao, Y. H., J. S. Lei, W. Y. Liu, Q. Li, and M. L. Chen. 2014. “Building Emotional Dictionary for Sentiment Analysis of Online News.” World Wide Web-Internet and Web Information Systems 17 (4): 723–742. https://doi.org/10.1007/s11280-013-0221-9.
  • Sankar, H., V. Subramaniyaswamy, V. Vijayakumar, S. A. Kumar, R. Logesh, and A. Umamakeswari. 2020. “Intelligent Sentiment Analysis Approach Using Edge Computing-Based Deep Learning Technique.” Software-Practice & Experience 50 (5): 645–657. https://doi.org/10.1002/spe.2687.
  • Song, W. Y., X. G. Ming, and Z. T. Xu. 2013. “Integrating Kano Model and Grey-Markov Chain to Predict Customer Requirement States.” Proceedings of the Institution of Mechanical Engineers Part B-Journal of Engineering Manufacture 227 (8): 1232–1244. https://doi.org/10.1177/0954405413485365.
  • Sun, Q., J. W. Niu, Z. Yao, and H. Yan. 2019. “Exploring eWOM in Online Customer Reviews: Sentiment Analysis at a Fine-Grained Level.” Engineering Applications of Artificial Intelligence 81:68–78. https://doi.org/10.1016/j.engappai.2019.02.004.
  • Takai, S., T. Yang, and J. A. Cafeo. 2011. “A Bayesian Method for Predicting Future Customer Need Distributions.” Concurrent Engineering Research and Applications 19 (3): 255–264. https://doi.org/10.1177/1063293X11418135.
  • Tang, M., J. Jin, Y. Liu, C. P. Li, and W. W. Zhang. 2019. “Integrating Topic, Sentiment, and Syntax for Modeling Online Reviews: A Topic Model Approach.” Journal of Computing and Information Science in Engineering 19 (1): 011001. https://doi.org/10.1115/1.4041475.
  • Tian, Y. T., S. X. Song, D. Zhou, and C. Wei. 2022. “Configuring Products Using NSGA-II: Warranty Profits, Performance-Price, and Environmental Emissions from the Contractor Perspective.” Journal of Engineering Design 33 (8-9): 545–566. https://doi.org/10.1080/09544828.2022.2078660.
  • Timoshenko, A., and J. R. Hauser. 2019. “Identifying Customer Needs from User-Generated Content.” Marketing Science 38 (1): 1–20. https://doi.org/10.1287/mksc.2018.1123.
  • Tran, T. K., and T. T. Phan. 2019. “Deep Learning Application to Ensemble Learning-the Simple, but Effective, Approach to Sentiment Classifying.” Applied Sciences Basel 9 (13): 2760. https://doi.org/10.3390/app9132760.
  • Wang, W. X., Y. Feng, and W. Q. Dai. 2018. “Topic Analysis of Online Reviews for Two Competitive Products Using Latent Dirichlet Allocation.” Electronic Commerce Research and Applications 29:142–156. https://doi.org/10.1016/j.elerap.2018.04.003.
  • Wang, W. M., Z. Li, L. Liu, Z. G. T. Tian, and E. Tsui. 2018. “Mining of Affective Responses and Affective Intentions of Products from Unstructured Text.” Journal of Engineering Design 29 (7): 404–429. https://doi.org/10.1080/09544828.2018.1448054.
  • Wang, W. M., Z. G. Tian, Z. Li, J. W. Wang, A. V. Barenji, and M. N. Cheng. 2019. “Supporting the Construction of Affective Product Taxonomies from Online Customer Reviews: An Affective-Semantic Approach.” Journal of Engineering Design 30 (10-12): 455–476. https://doi.org/10.1080/09544828.2019.1642460.
  • Wei, W., X. Y. Cao, H. Li, L. J. Shen, Y. Q. Feng, and P. A. Watters. 2022. “Improving Speech Emotion Recognition Based on Acoustic Words Emotion Dictionary.” Natural Language Engineering 27 (6): 747–761. https://doi.org/10.1017/S1351324920000339.
  • Xiao, Y., Z. Huang, Q. Li, X. Lu, and T. Li. 2022. “Diffusion Pixelation: A Game Diffusion Model of Rumor & Anti-Rumor Inspired by Image Restoration.” IEEE Transactions on Knowledge and Data Engineering 35 (5): 4682–4694. https://doi.org/10.1109/TKDE.2022.3144310.
  • Xiao, Y., B. Li, and Z. Gong. 2018. “Real-time Identification of Urban Rainstorm Waterlogging Disasters Based on Weibo Big Data.” Natural Hazards 94 (2): 833–842. https://doi.org/10.1007/s11069-018-3427-4.
  • Xing, T., G. Wang, L. Yuan, Y. S. Liu, X. P. Ye, and J. J. Zhao. 2020. “A Systematic Estimation Approach for the Importance of Engineering Characteristics Based on Online Reviews.” Proceedings of the Institution of Mechanical Engineers Part B-Journal of Engineering Manufacture 234 (11): 1433–1447. https://doi.org/10.1177/0954405420918156.
  • Yusuf-Asaju, A. W., Z. B. Dahalin, and A. Ta'a. 2019. “Towards Real-time Customer Satisfaction Prediction Model for Mobile Internet Networks.” Recent Trends in Data Science and Soft Computing 843:95–104. https://doi.org/10.1007/978-3-319-99007-1_10.
  • Zhang, M., B. Fan, N. Zhang, W. J. Wang, and W. G. Fan. 2021. “Mining Product Innovation Ideals from Online Reviews.” Information Processing and Management 58 (1): 102389. https://doi.org/10.1016/j.ipm.2020.102389.

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