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26 May 2023 Review rating model based on subjective vocabulary in user reviews
Fanxing Zeng
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Proceedings Volume 12700, International Conference on Electronic Information Engineering and Data Processing (EIEDP 2023); 127000R (2023) https://doi.org/10.1117/12.2682344
Event: International Conference on Electronic Information Engineering and Data Processing (EIEDP 2023), 2023, Nanchang, China
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.
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Fanxing Zeng "Review rating model based on subjective vocabulary in user reviews", Proc. SPIE 12700, International Conference on Electronic Information Engineering and Data Processing (EIEDP 2023), 127000R (26 May 2023); https://doi.org/10.1117/12.2682344
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KEYWORDS
Data modeling
Quantitative analysis
Modeling
Linear regression
Analytical research
Mining
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