온라인 리뷰의 텍스트 분석을 통한 고객 평점 예측에 관한 연구
Customer Rating Prediction Using Text Analysis of Online Reviews
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초록
최근 온라인 리뷰의 중요성이 강조됨에 따라, 온라인 리뷰와 고객 평점의 관계로부터 제품 및 서비스의 품질속성을 도출하는 방안에 대한 연구가 이루어지고 있다. 그러나, 많은 온라인 리뷰에서는 일관된 기준의 고객 평점이 존재하지 않아 평점 기반의 방법이 적용되기 어렵다. 이에 따라 본 연구에서는 텍스트 분석을 활용하여 고객 평점에 의존하지 않고 Kano 품질속성과 극성 점수에 기반한 고객 평점 예측 모형을 제시하였다. 이를 통해 얻어진 평점의 실제 온라인 리뷰에 대한 결정계수가 유의미하게 나타남을 확인하여, 본 연구에서 제시한 Kano 품질속성을 기반으로 한 고객 평점 예측 모형의 유효성을 입증하였다. 나아가 동일한 Kano 품질속성을 가지는 호텔 성급을 그룹화 하여 각 그룹별로 Kano 품질속성의 특성을 고려하는 경우에 예측력이 더욱 높아짐을 확인하여, 차별화된 Kano 품질속성을 적극적으로 평점 예측에 고려하는 것이 예측력을 높이는 데에 기여할 수 있음을 확인하였다. 이러한 결과는 평점이 명시적으로 나타나 있지 않은 고객 리뷰에서 고객들이 잠재적으로 평가하는 평점을 정량화할 수 있음을 입증하여 실용적 의미가 크다.
Abstract
The usefulness of online reviews in understanding customers’ perceptions of quality satisfaction attributes has prompted a growing number of studies to present ways to predict customer ratings based on online review texts. However, these studies rely on the relationship between review texts and customer ratings, which cannot be applied to reviews without explicitly stated rating values. In this study, we propose a model to predict customer ratings from online review texts without explicit customer ratings. We applied text analysis to hotel review texts collected on the internet and extracted the Kano quality attributes of hotel services, based on which we established a model to predict potential customer ratings. Confirming that the coefficient of determination for the actual online reviews of the ratings obtained with this model is significant, we demonstrated the validity of the prediction model for customer ratings based on the Kano quality attributes. Furthermore, after grouping hotel star grades having identical Kano quality attributes, we confirmed that predictive power is further enhanced when the Kano quality attributes differentiated by group are considered. Thus, we confirmed that actively considering the specialized Kano quality attributes in the prediction of ratings contributes to enhancing the predictive power. These findings suggest that it is possible to quantify the ratings customers award in their reviews where rating scores are not explicitly shown, which indicates substantial significance for practical use.
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