2020
DOI: 10.1177/0954405420918156
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A systematic estimation approach for the importance of engineering characteristics based on online reviews

Abstract: Online reviews are a new source for the valuable voice of customers. By identifying the customer’s opinion, designers can comprehend the important features of a product to satisfy customer demand, thus enhancing the market competitiveness of the product. Customers have opinions on multiple aspects of products hidden in reviews, and sentiment divergence may exist. Moreover, there is a gap between customer requirements and the product’s system requirements. How to effectively analyze a large number of reviews to… Show more

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Cited by 6 publications
(4 citation statements)
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“…However, they ignore CRs. As one of the typical customer-driven methods, QFD is effective to identify the correlations among CRs and RFs and prioritize RFs considering CRs (Xing et al. , 2020).…”
Section: Introductionmentioning
confidence: 99%
See 1 more Smart Citation
“…However, they ignore CRs. As one of the typical customer-driven methods, QFD is effective to identify the correlations among CRs and RFs and prioritize RFs considering CRs (Xing et al. , 2020).…”
Section: Introductionmentioning
confidence: 99%
“…However, they ignore CRs. As one of the typical customer-driven methods, QFD is effective to identify the correlations among CRs and RFs and prioritize RFs considering CRs (Xing et al, 2020). However, some inherent deficiencies are existing in the traditional QFD, which may cause great impact on the priority of RFs.…”
Section: Introductionmentioning
confidence: 99%
“…Li et al (2022) applied word association model to capture CRs from online customer reviews. Xing et al (2020) applied convolutional neural network and sentiment analysis to capture CRs for a hair dryer. Geng et al (2020) constructed capture method based on customers' satisfaction.…”
Section: Introductionmentioning
confidence: 99%
“…Li et al [4] applied word association model to capture CRs from online customer reviews. Xing et al [5] applied convolutional neural network and sentiment analysis to capture CRs for a hair dryer. Geng et al [6] constructed capture method based on customers' satisfaction.…”
mentioning
confidence: 99%