Identification of the to-be-improved product features based on online reviews for product redesign
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DOI: 10.1080/00207543.2018.1521019
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Cited by:
- Chao He & Zhongkai Li & Dengzhuo Liu & Guangyu Zou & Shuai Wang, 2023. "Improving the functional performances for product family by mining online reviews," Journal of Intelligent Manufacturing, Springer, vol. 34(6), pages 2809-2824, August.
- Jindong Qin & Pan Zheng & Xiaojun Wang, 2024. "Product Redesign and Innovation Based on Online Reviews: A Multistage Combined Search Method," INFORMS Journal on Computing, INFORMS, vol. 36(3), pages 742-765, May.
- Xiao, Yan & Li, Congdong & Thürer, Matthias & Liu, Yide & Qu, Ting, 2022. "User preference mining based on fine-grained sentiment analysis," Journal of Retailing and Consumer Services, Elsevier, vol. 68(C).
- Zhen-Yu Chen & Xin-Li Liu & Li-Ping Yin, 2023. "Data-driven product configuration improvement and product line restructuring with text mining and multitask learning," Journal of Intelligent Manufacturing, Springer, vol. 34(4), pages 2043-2059, April.
- Huang, Shupeng & Potter, Andrew & Eyers, Daniel & Li, Qinyun, 2021. "The influence of online review adoption on the profitability of capacitated supply chains," Omega, Elsevier, vol. 105(C).
- Hanyang Luo & Wugang Song & Wanhua Zhou & Xudong Lin & Sumin Yu, 2023. "An Analysis Framework to Reveal Automobile Users’ Preferences from Online User-Generated Content," Sustainability, MDPI, vol. 15(18), pages 1-29, September.
- Yakubu, Hanan & Kwong, C.K., 2021. "Forecasting the importance of product attributes using online customer reviews and Google Trends," Technological Forecasting and Social Change, Elsevier, vol. 171(C).
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