The structural topic model for online review analysis Comparison between green and non-green restaurants
- Authors
- Park, Eunhye (Olivia); Chae, Bongsug (Kevin); Kwon, Junehee
- Issue Date
- Jul-2018
- Publisher
- EMERALD GROUP PUBLISHING LTD
- Keywords
- User-generated content; Green practices; Topic modeling; Covariate; Structural topic modeling
- Citation
- JOURNAL OF HOSPITALITY AND TOURISM TECHNOLOGY, v.11, no.1, pp.1 - 17
- Journal Title
- JOURNAL OF HOSPITALITY AND TOURISM TECHNOLOGY
- Volume
- 11
- Number
- 1
- Start Page
- 1
- End Page
- 17
- URI
- https://scholarworks.bwise.kr/gachon/handle/2020.sw.gachon/80520
- DOI
- 10.1108/JHTT-08-2017-0075
- ISSN
- 1757-9880
- Abstract
- Purpose 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). Design/methodology/approach 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. Findings 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. Research limitations/implications This study demonstrates the application of STM for the systematic analysis of a large amount of text data. Originality/value 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.
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Collections - 바이오나노대학 > 식품영양학과 > 1. Journal Articles
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