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

Hengyun Li, Bruce X.B. Yu, Gang Li, Huicai Gao

Research output: Journal article publicationJournal articleAcademic researchpeer-review

Abstract

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.

Original languageEnglish
Article number104707
JournalTourism Management
Volume96
DOIs
Publication statusPublished - Jun 2023

Keywords

  • Aspect-based sentiment analysis
  • Business survival
  • Online review
  • Restaurant
  • User-generated content

ASJC Scopus subject areas

  • Development
  • Transportation
  • Tourism, Leisure and Hospitality Management
  • Strategy and Management

Fingerprint

Dive into the research topics of 'Restaurant survival prediction using customer-generated content: An aspect-based sentiment analysis of online reviews'. Together they form a unique fingerprint.

Cite this