텍스트 마이닝을 활용한 고객 리뷰의 유용성 지수 개선에 관한 연구

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이홍주
Lee, Hong Joo

  • 투고 : 2015.07.20
  • 심사 : 2015.09.29
  • 발행 : 2015.12.31

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.

키워드

Customer Review;Classification;Usefulness index

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과제정보

연구 과제 주관 기관 : 한국연구재단