Journal of Information Technology Applications and Management

Korea Data Strategy Society (한국데이터전략학회)
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Methodology for Deriving Required Quality of Product Using Analysis of Customer Reviews

사용자 리뷰 분석을 통한 제품 요구품질 도출 방법론

  • Yerin Yu (Graduate School of Business IT, Kookmin University) ;
  • Jeongeun Byun (R&BD Analysis Research Team, Technology Commercialization Research Center, Korea Institute of Science and Technology Information ) ;
  • Kuk Jin Bae (R&BD Analysis Research Team, Technology Commercialization Research Center, Korea Institute of Science and Technology Information) ;
  • Sumin Seo (R&BD Analysis Research Team, Technology Commercialization Research Center, Korea Institute of Science and Technology Information) ;
  • Younha Kim (Graduate School of Business IT, Kookmin University) ;
  • Namgyu Kim (Graduate School of Business IT, Kookmin University)
  • Received : 2023.01.25
  • Accepted : 2023.02.20
  • Published : 2023.04.30
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Abstract

Recently, as technology development has accelerated and product life cycles have been shortened, it is necessary to derive key product features from customers in the R&D planning and evaluation stage. More companies want differentiated competitiveness by providing consumer-tailored products based on big data and artificial intelligence technology. To achieve this, the need to correctly grasp the required quality, which is a requirement of consumers, is increasing. However, the existing methods are centered on suppliers or domain experts, so there is a gap from the actual perspective of consumers. In other words, product attributes were defined by suppliers or field experts, but this may not consider consumers' actual perspective. Accordingly, the demand for deriving the product's main attributes through reviews containing consumers' perspectives has recently increased. Therefore, we propose a review data analysis-based required quality methodology containing customer requirements. Specifically, a pre-training language model with a good understanding of Korean reviews was established, consumer intent was correctly identified, and key contents were extracted from the review through a combination of KeyBERT and topic modeling to derive the required quality for each product. RevBERT, a Korean review domain-specific pre-training language model, was established through further pre-training. By comparing the existing pre-training language model KcBERT, we confirmed that RevBERT had a deeper understanding of customer reviews. In addition, all processes other than that of selecting the required quality were linked to the automation process, resulting in the automation of deriving the required quality based on data.

Keywords

Acknowledgement

This work was supported by the Korea Institute of Science and Technology Information (K-23-L03-C03-S01).

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