Full metadata record
DC Field | Value | Language |
---|---|---|
dc.contributor.author | 정준각 | - |
dc.date.accessioned | 2022-10-31T06:33:21Z | - |
dc.date.available | 2022-10-31T06:33:21Z | - |
dc.date.issued | 2021-02 | - |
dc.identifier.citation | JOURNAL OF MECHANICAL DESIGN, v. 143, no. 8, article no. 081705 | en_US |
dc.identifier.issn | 1050-0472 ; 1528-9001 | en_US |
dc.identifier.uri | https://asmedigitalcollection.asme.org/mechanicaldesign/article/143/8/081705/1096683/Approach-for-Importance-Performance-Analysis-of | en_US |
dc.identifier.uri | https://repository.hanyang.ac.kr/handle/20.500.11754/176193 | - |
dc.description.abstract | The importance–performance analysis (IPA) is a widely used technique to guide strategic planning for the improvement of customer satisfaction. Compared with surveys, numerous online reviews can be easily collected at a lower cost. Online reviews provide a promising source for the IPA. This paper proposes an approach for conducting the IPA from online reviews for product design. Product attributes from online reviews are first identified by latent Dirichlet allocation. The performance of the identified attributes is subsequently estimated by the aspect-based sentiment analysis of IBM Watson. Finally, the importance of the identified attributes is estimated by evaluating the effect of sentiments of each product attribute on the overall rating using an explainable deep neural network. A Shapley additive explanation-based method is proposed to estimate the importance values of product attributes with a low variance by combining the effect of the input features from multiple optimal neural networks with a high performance. A case study of smartphones is presented to demonstrate the proposed approach. The performance and importance estimates of the proposed approach are compared with those of previous sentiment analysis and neural network-based method, and the results exhibit that the former can perform IPA more reliably. The proposed approach uses minimal manual operation and can support companies to take decisions rapidly and effectively, compared with survey-based methods. | en_US |
dc.description.sponsorship | This research was supported by the Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (2019R1I1A1A01063298). | en_US |
dc.language | en | en_US |
dc.publisher | ASME | en_US |
dc.subject | data-driven design; interpretable machine learning; neural network | en_US |
dc.title | Approach for Importance–Performance Analysis of Product Attributes From Online Reviews | en_US |
dc.type | Article | en_US |
dc.identifier.doi | 10.1115/1.4049865 | en_US |
dc.relation.journal | JOURNAL OF MECHANICAL DESIGN | - |
dc.contributor.googleauthor | Joung, Junegak | - |
dc.contributor.googleauthor | Kim, Harrison M. | - |
dc.relation.code | 2021009133 | - |
dc.sector.campus | S | - |
dc.sector.daehak | SCHOOL OF INTERDISCIPLINARY INDUSTRIAL STUDIES[S] | - |
dc.sector.department | SCHOOL OF INTERDISCIPLINARY INDUSTRIAL STUDIES | - |
dc.identifier.pid | june30 | - |
dc.identifier.orcid | https://orcid.org/0000-0003-3595-3349 | - |
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