Grounded Theory Analysis of Online Shopping Reviews Based on Text Clustering
Main Article Content
Abstract
Grounded Theory Analysis of online shopping reviews can improve the quality and accuracy of review data analysis, providing valuable information and suggestions for relevant businesses and consumers. This article focuses on analyzing mobile phone reviews on JD.com as coding objects, effectively employing machine learning algorithms such as Latent Semantic Indexing (LSI), Latent Dirichlet Allocation (LDA), and text clustering in natural language processing to extract thematic keywords from online shopping review texts and perform axial coding. Subsequently, DBSCAN clustering algorithm is applied to categorize the main categories of design, service, and production under the perspective of businesses and consumers as consumption attraction, while categorizing price and brand under consumption intention, forming selective coding. By applying Grounded Theory to the analysis of online shopping reviews, this study accurately identifies consumer needs and feedback, with the aim of helping businesses improve product and service quality, enhance customer satisfaction and loyalty, and engage in personalized marketing and customized services.
Article Details
This work is licensed under a Creative Commons Attribution-NoDerivatives 4.0 International License.
References
Jay Kumar, Junming Shao, Rajesh Kumar, Salah Ud Din, Cobbinah B. Mawuli, Qinli Yang, "A context-enhanced Dirichlet model for online clustering in short text streams," Expert Syst. Appl. vol. 228, Oct. 2023, doi:10.1016/j.eswa.2023.120262.
Laith Abualigah, Khaled H. Almotairi, Mohammed A.A. Al-qaness, Ahmed A. Ewees, Dalia Yousri, Mohamed Abd Elaziz, Mohammad H. Nadimi-Shahraki, "Efficient text document clustering approach using multi-search Arithmetic Optimization Algorithm," Knowledge-Based Systems, vol. 248, 2022, doi:10.1016/j.knosys.2022.108833.
Jingzhu Wei, Huizi Cao, and Lele Zhang, “Research on Users' Needs of Public Culture Cloud Activities Based on Online Comments,” Library and Information Service, vol. 66, no. 9, pp. 66-73, 2022.
Wei-Wei Qiu, Hai-Tao Yu, Chin-Hsun (Ken) Tsai, Di (Judy) Zhu, Ming-Hsiang Chen, Hyun Jeong Kim, “Understanding the value of host-guest intimacy behind online reviews of Airbnb,” International Journal of Hospitality Management, vol. 115, 2023, doi:10.1016/j.ijhm.2023.103599.
Park, J., Li, X., Li, Q., Kim, J. "Impact on recommendation performance of online review helpfulness and consistency." Data technologies and applications, vol. 57, pp. 199-221, 2023, doi:10.1108/dta-04-2022-0172.
Miriam Alzate, Marta Arce-Urriza, Javier Cebollada, "Is review visibility fostering helpful votes? The role of review rank and review characteristics in the adoption of information," Computers in Human Behavior, vol. 153, 2024, doi:10.1016/j.chb.2023.108088.
Pradeep Racherla and Wesley Friske, "Perceived 'usefulness' of online consumer reviews: An exploratory investigation across three services categories." Electronic Commerce Research and Applications, vol. 11, Issue 6, pp. 548-559, 2012, doi:10.1016/j.elerap.2012.06.003.
David Robert Stöckli and Hamid Khobzi, "Recommendation systems and convergence of online reviews: The type of product network matters!" Decision Support Systems, vol. 142, 2021, doi:10.1016/j.dss.2020.113475.
Hengyun Li, Hongbo Liu, Hyejo Hailey Shin, and Haipeng Ji, "Impacts of user-generated images in online reviews on customer engagement: A panel data analysis." Tourism Management, vol. 101, 2024, doi:10.1016/j.tourman.2023.104855.
Mingming Cheng, Xin Jin, "What do Airbnb users care about? An analysis of online review comments." International Journal of Hospitality Management, vol. 76(A), pp. 58-70, 2019, doi:10.1016/j.ijhm.2018.04.004.
Wenyan Song, Wan Rong, and Yuqi Tang, "Quantifying risk of service failure in customer complaints: A textual analysis-based approach." Advanced Engineering Informatics, Vol. 60, 2024,, doi:10.1016/j.aei.2024.102377.
Kostyra, D., Reiner, J., Natter, M., & Klapper, D. "Decomposing the effects of online customer reviews on brand, price, and product attributes." International Journal of Research in Marketing, vol. 33(1), pp. 11–26, 2016.
Babak Amiri, Ramin Karimianghadim, "A novel text clustering model based on topic modelling and social network analysis," Chaos, Solitons & Fractals, vol. 181, 2024, doi:10.1016/j.chaos.2024.114633.
Laith Abualigah, Khaled H. Almotairi, Mohammed A.A. Al-qaness, Ahmed A. Ewees, Dalia Yousri, Mohamed Abd Elaziz, Mohammad H. Nadimi-Shahraki, "Efficient text document clustering approach using multi-search Arithmetic Optimization Algorithm," Knowledge-Based Systems, vol. 248, 2022, doi:10.1016/j.knosys.2022.108833.
Lin Wang, Huaxia Gao, and Yang Zhao, “Research on online shopping contextual cues: refining classification from text mining,” Asia Pacific Journal of Marketing and Logistics, vol. ahead-of-print, 2022, doi:10.1108/APJML-10-2022-0847.
Bin Zhu, Dingfei Guo, and LongRen, “Consumer Preference Analysis based on Text Comments and Ratings: A Multi-attribute Decision-making Perspective.” Information and Management, vol. 59, issue 3, April 2022.
Alexis Papathanassis, Friederike Knolle, “Exploring the adoption and processing of online holiday reviews: A grounded theory approach,” Tourism Management, vol. 32, Issue 2, pp. 215-224, 2011, doi:10.1016/j.tourman.2009.12.005.
Xavier Matteucci and Juergen Gnoth, “Elaborating on grounded theory in tourism research,” Annals of Tourism Research, vol. 65, pp. 49-59, 2017.
Junhui Cheng, Mei Li, "Is There Any Change in Public Comments as the Event Develops? -- The Generative Mechanism of Weibo Users' Commentary Behavior in Multi-Contextual Public Opinion Based on Grounded Theory", Journal of Intelligence, vol. 40, no. 10, pp. 45-51+65, 2021.
Hou J., Zhu A., "Identification of Usefulness for Online Reviews Based on Grounded Theory and Multilayer Perceptron Neural Network." Applied Sciences, vol. 13, no. 9 pp. 5321, 2023.