Asia-Pacific Journal of Business Venturing and Entrepreneurship (벤처창업연구)

The Korean Society of Business Venturing (한국벤처창업학회)
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A Study on Machine Learning-Based Modelling of Online Review Sentiment Analysis

머신러닝 기반 온라인 리뷰 감성 분석 모델링에 대한 연구

  • 김민수 (한성대학교 IT공과대학 컴퓨터공학부) ;
  • 김주희 (동덕여자대학교 문화지식융합대학)
  • Received : 2024.09.20
  • Accepted : 2024.10.21
  • Published : 2024.10.31
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Abstract

Online reviews play a crucial role in assessing a company's market value and are a significant factor influencing profitability. As such, sentiment analysis of online reviews has emerged as a key indicator for predicting business success. This study focuses on restaurant reviews from Yelp, one of the leading online review platforms, utilizing the Yelp Open Dataset. Six machine learning algorithms were applied to predict the sentiment polarity of these reviews: Logistic Regression, Support Vector Machine (SVM), Random Forest, Gradient Boosting Machine (GBM), XGBoost, and LightGBM. Performance evaluations demonstrated that Logistic Regression, SVM, and LightGBM achieved the highest accuracy, with a score of 0.91. The primary contribution of this study is its ability to transform unstructured review text into quantifiable data, enabling businesses, especially startups, to effectively analyze customer feedback and predict ratings. These insights are expected to assist business owners in forecasting consumer behavior and developing strategic marketing approaches.

온라인 리뷰는 시장 내에서의 기업의 가치를 평가하는 데 있어 중요한 역할을 하며, 기업의 수익에 큰 영향을 미치는 요인 중 하나이다. 따라서 온라인 리뷰의 감성 분석 지표는 사업의 성공을 예측할 수 있는 중요한 지표 중 하나이다. 본 연구에서는 대표적인 온라인 리뷰 플랫폼 중의 하나인 Yelp 플랫폼에 있는 레스토랑 리뷰 텍스트를 연구대상으로 선정하였고, Yelp Open Dataset에서 제공하는 리뷰 데이터 세트를 활용하였다. 본 연구에서는 레스토랑 리뷰의 Polarity Prediction을 위해 Logistic Regression, SVM, Random Forest, Gradient Boosting Machine(GBM), XGBoost, LightGBM 총 6가지 머신러닝 알고리즘을 사용하여 연구를 진행하였다. 각 모델의 성능평가 결과, Logistic Regression, SVM, LightGBM 알고리즘이 0.91로 가장 정확도가 높게 나타났다. 본 연구는 비정형화된 형태로 작성된 텍스트의 리뷰 데이터를 정량화하여 평점으로 예측할 수 있도록 하여 스타트업을 포함한 기업이 고객 피드백을 효과적으로 분석할 수 있도록 한다는 점에서 공헌점이 있다, 나아가 비즈니스 운영자들이 소비자 행동을 예측하고, 마케팅 전략 수립에 활용할 수 있는 유용한 인사이트를 제공할 수 있을 것으로 기대된다.

Keywords

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