Earticle

초록

영어

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.

목차

Abstract
1. 서론
2. 관련 연구
2.1 Topic Modeling
2.2 Keyword Extraction
2.3 Pre-trained Language Model and Further Pre-training
3. 제안 방법론
3.1 Overall Research Process
3.2 Data Pre-processing
3.3 Building RevBERT by Further Pre-training
3.4 Key-phrase and Keyword Extraction
3.5 Required Quality Generation
4. 실험
4.1 Overview of Experiments
4.2 Results of Pre-processing and Data Split
4.3 Performance Evaluation
4.4 Results of Key-phrase and Keyword Extraction
4.5 Results of Required Quality Generation
5. 결론
References

키워드

저자

• 유예린 [ Yerin Yu | M.S. candidate, Graduate School of Business IT, Kookmin University ] First Author
• 변정은 [ Jeongeun Byun | Director, R&BD Analysis Research Team, Technology Commercialization Research Center, Korea Institute of Science and Technology Information ] Corresponding Author
• 배국진 [ Kuk Jin Bae | Principal Researcher, R&BD Analysis Research Team, Technology Commercialization Research Center, Korea Institute of Science and Technology Information ] Co-Author
• 서수민 [ Sumin Seo | Technologist, R&BD Analysis Research Team, Technology Commercialization Research Center, Korea Institute of Science and Technology Information ] Co-Author
• 김윤하 [ Younha Kim | M.S. candidate, Graduate School of Business IT, Kookmin University ] Co-Author
• 김남규 [ Namgyu Kim | Professor, Graduate School of Business IT, Kookmin University ] Co-Author

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  JITAM [Journal of Information Technology Applications and Management]

• 간기

  격월간

• pISSN

  1598-6284

• eISSN

  2508-1209

• 수록기간

  1999~2026

• 십진분류

  KDC 005 DDC 005