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。
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。
Park, E., Chae, B. and Kwon, J. (2018), "The structural topic model for online review analysis", Journal of Hospitality and Tourism Technology, Vol. ahead-of-print No. ahead-of-print. https://doi.org/10.1108/JHTT-08-2017-0075Download as .RIS
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