The structural topic model for online review analysis: Comparison between green and non-green restaurants

The purpose of this study was to explore influences of review-related information on topical proportions and the pattern of word appearances in each topic (topical content) using structural topic model (STM).

For 173,607 Yelp.com reviews written in 2005-2016, STM-based topic modeling was applied with inclusion of covariates in addition to traditional statistical analyses.

Differences in topic prevalence and topical contents were found between certified green and non-certified restaurants. Customers’ recognition in sustainable food topics were changed over time.

This study demonstrates the application of STM for the systematic analysis of a large amount of text data.

Limited study in the hospitality literature examined the influence of review-level metadata on topic and term estimation. Through topic modeling, customers’ natural responses toward green practices were identified.

本研究旨在通过结构性话题建模（STM）方法以开拓评论性内容对于话题组成和词条构成的影响。

本论文采用 173，607 份 Yelp.com 在 2015 至 2016 年间的评论内容为样本，STM 分析结合共变量形成话题性建模。

话题趋势和话题内容的不同存在于认证过的绿色餐馆与非认证的绿色餐馆中。消费者对于可持续性的食物话题兴趣随着时间而改变。

本研究对 STM 相关大规模文本型数据的系统分析方法给与启示。

在酒店管理文献中很少有文章研究评论性元数据对于话题和词条预估的影响。通过话题建模，消费者对于绿色措施的反馈获得了梳理和确认。