텍스트 마이닝을 활용한 고객 리뷰의 유용성 지수 개선에 관한 연구

• 영문명: A Study on Classifications of Useful Customer Reviews by Applying Text Mining Approach
• 발행기관: 한국IT서비스학회
• 저자명: 이홍주(Hong Joo Lee)
• 간행물 정보: 『한국IT서비스학회지』한국IT서비스학회지 제14권 제4호, 159~169쪽, 전체 11쪽
• 주제분류: 경제경영 > 경영학
• 파일형태: PDF
• 발행일자: 2015.12.30

영문 초록

Customer reviews are one of the important sources for purchase decision makings in online stores. Online stores have tried to provide useful reviews in product pages to customers. To assess the usefulness of customer reviews before other users have voted enough on the reviews, diverse aspects of reviews were utilized in prevous studies. Style and semantic information were utilized in many studies. This study aims to test diverse alogrithms and datasets for identifying a proper classification method and threshold to classify useful reviews. In particular, most researches utilized ratio type helpfulness index as Amazon.com used. However, there is another type of usefulness index utilized in TripAdviser.com or Yelp.com, count type helpfulness index. There was no proper threshold to classify useful reviews yet for count type helpfulness index. This study used reivews and their usefulness votes on restaurnats from Yelp.com to devise diverse datasets and applied text mining approaches to classify useful reviews. Random Forest, SVM, and GLMNET showed the greater values of accuracy than other approaches.

목차

Abstract

1. 서론

2. 관련 연구

3. 자료

4. 분류 방안 및 결과

5. 토의 및 결론

References

키워드

Customer Review Classification Usefulness index