Restaurant survival prediction using customer-generated content: An aspect-based sentiment analysis of online reviews

被引：63
作者：
Li, Hengyun [1 ]
Yu, Bruce X. B. [2 ]
Li, Gang [3 ]
Gao, Huicai [1 ]
机构：
[1] Hong Kong Polytech Univ, Sch Hotel & Tourism Management, Hong Kong, Peoples R China
[2] Hong Kong Polytech Univ, Dept Comp, Hong Kong, Peoples R China
[3] Univ Surrey, Sch Hospitality & Tourism Management, Guildford GU2 7XH, Surrey, England
来源：
TOURISM MANAGEMENT | 2023年 / 96卷
基金：
中国国家自然科学基金;
关键词：
User-generated content; Business survival; Aspect-based sentiment analysis; Online review; Restaurant; WORD-OF-MOUTH; BUSINESS FAILURE; SOCIAL MEDIA; HOTEL; BANKRUPTCY; MODEL; SATISFACTION; HOSPITALITY; PERFORMANCE; EXPERIENCE;
D O I：
10.1016/j.tourman.2022.104707
中图分类号：
X [环境科学、安全科学];
学科分类号：
08 ; 0830 ;
摘要：
Business failure prediction or survival analysis can assist corporate organizations in better understanding their performance and improving decision making. Based on aspect-based sentiment analysis (ABSA), this study investigates the effect of customer-generated content (i.e., online reviews) in predicting restaurant survival using datasets for restaurants in two world famous tourism destinations in the United States. ABSA divides the overall review sentiment of each online review into five categories, namely location, tastiness, price, service, and atmosphere. By employing the machine learning-based conditional survival forest model, empirical results show that compared with overall review sentiment, aspect-based sentiment for various factors can improve the prediction performance of restaurant survival. Based on feature importance analysis, this study also highlights the effects of different types of aspect sentiment on restaurant survival prediction to identify which features of online reviews are optimal indicators of restaurant survival.
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页数：16
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