Review rating model based on subjective vocabulary in user reviews

Abstract

A problem of using the user review for scoring restaurants from Yelp’s dataset of restaurant business is discussed. Reviews posted by users on the same product or service are diverse and subjective. The sentiment and focus of user reviews are more likely to be subjective even for the same product and service. A review rating model with subjective tendencies of users is constructed through using sentiment classification and cluster analysis to analyze the subjective vocabularies and sentiment coefficients in reviews. The model identifies the terms of different categories in user reviews, quantifies and analyzes them, combines the sentiment with categories, and finally selects the rating of the restaurant as the dependent variable and the elements including the food quality, the restaurant ambience, the service and the grade of recommendation as independent variables before constructing user ratings by using a traditional least squares multiple linear regression model.