A heterogeneous opinion-driven decision-support model for tourists’ selection with different travel needs in online reviews

Adjei Peter Darko and Decui Liang

Journal of the Operational Research Society, 2023, vol. 74, issue 1, 272-289

Abstract: The advancement of tourism websites has greatly improved the travelling experiences of tourists. One such way is the recommendation of restaurants by tourism websites. As a result, online restaurant reviews have grown tremendously in recent times. Using online reviews of restaurants in Ghana, this article deeply examines tourists’ restaurant experiences. Specifically, we employ unsupervised machine learning techniques such as Term Frequency-Inverse Document Frequency (TF-IDF) and K-means algorithms to detect restaurant factors and evaluation attributes. We further develop an improved probabilistic linguistic linear programming technique for multidimensional analysis of preference (PL-LINMAP) to derive the attributes’ weight importance for different tourist groups. Additionally, we propose an uncertain decision-support model known as probabilistic linguistic Measurement Alternatives and Ranking according to the COmpromise Solution (PL-MARCOS) to aid different tourist groups in satisfactory restaurant selection. This study provides a comprehensive model for restaurant managers in understanding heterogeneous tourist preferences.

Date: 2023
References: Add references at CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
http://hdl.handle.net/10.1080/01605682.2022.2035274 (text/html)
Access to full text is restricted to subscribers.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:taf:tjorxx:v:74:y:2023:i:1:p:272-289

Ordering information: This journal article can be ordered from
http://www.tandfonline.com/pricing/journal/tjor20

DOI: 10.1080/01605682.2022.2035274

Access Statistics for this article

Journal of the Operational Research Society is currently edited by Tom Archibald

More articles in Journal of the Operational Research Society from Taylor & Francis Journals
Bibliographic data for series maintained by Chris Longhurst ().