Abstract
The purpose of this study is to explore the effect of different types of simultaneous presentation (i.e., reviewer information, textual and visual content, and similarity between textual-visual contents) on review usefulness and review enjoyment in online restaurant reviews (ORRs), as they are interrelated yet have rarely been examined together in previous research. By using Latent Dirichlet Allocation (LDA) topic modeling and state-of-the-art machine learning (ML) methodologies, we found that review readability in textual content and salient objects in images in visual content have a significant impact on both review usefulness and review enjoyment. Moreover, similarity between textual-visual contents was found to be a major factor in determining review usefulness but not review enjoyment. As for reviewer information, reputation, expertise, and location of residence, these were found to be significantly related to review enjoyment. This study contributes to the body of knowledge on ORRs and provides valuable implications for general users and managers in the hospitality and tourism industries.
Original language | English |
---|---|
Pages (from-to) | 181-202 |
Number of pages | 22 |
Journal | Asia Pacific Journal of Information Systems |
Volume | 29 |
Issue number | 2 |
DOIs | |
Publication status | Published - 1 Jun 2019 |
Bibliographical note
Publisher Copyright:© 2013 The Korean Society of Management Information Systems.
Keywords
- Image mining
- Machine learning
- Online restaurant review
- Simultaneous presentation
- Topic modeling