Restaurant survival prediction using machine learning: Do the variance and sources of customers’ online reviews matter?
研究利用波士顿2838家餐厅的在线评论数据，发现评论方差（如评分方差、情感方差）能显著提升机器学习模型对餐厅生存的预测能力，且专家评论在疫情前预测中表现更优。
Restaurant constitutes an essential part of the tourism industry . In times of uncertainty and transition, restaurant survival prediction is vital for deepening organizations' understanding of business performance and facilitating decisions. By tapping into online reviews , a prevalent form of user-generated content, this study identifies review variance as a leading indicator of restaurants’ survival drawing from data on 2838 restaurants in Boston and their corresponding reviews. Machine learning–based survival analysis shows that models integrating fine-grained review variance (i.e., review rating variance, overall review sentiment variance, and fine-grained review sentiment variance) outperform models that do not account for these factors in restaurant survival prediction before and during the pandemic. Furthermore, in most cases, expert reviews hold stronger predictive power for pre-pandemic restaurant survival than non-expert and all forms of reviews. This study contributes to the literature on business survival prediction and guides industry practitioners in monitoring and enhancing their enterprises .