A Model to Measure Tourist Preference toward Scenic Spots Based on Social Media Data: A Case of Dapeng in China

Yao Sun, Hang Ma and Edwin H. W. Chan
Additional contact information
Yao Sun: School of Architecture and Urban Planning, Shenzhen Graduate School, Harbin Institute of Technology, Shenzhen 518055, China
Hang Ma: School of Architecture and Urban Planning, Shenzhen Graduate School, Harbin Institute of Technology, Shenzhen 518055, China
Edwin H. W. Chan: Department of Building and Real Estate, The Hong Kong Polytechnic University, Hong Kong, China

Sustainability, 2017, vol. 10, issue 1, 1-13

Abstract: Research on tourist preference toward different tourism destinations has been a hot topic for decades in the field of tourism development. Tourist preference is mostly measured with small group opinion-based methods through introducing indicator systems in previous studies. In the digital age, e-tourism makes it possible to collect huge volumes of social data produced by tourists from the internet, to establish a new way of measuring tourist preference toward a close group of tourism destinations. This paper introduces a new model using social media data to quantitatively measure the market trend of a group of scenic spots from the angle of tourists’ demand, using three attributes: tourist sentiment orientation, present tourist market shares, and potential tourist awareness. Through data mining, cleaning, and analyzing with the framework of Machine Learning, the relative tourist preference toward 34 scenic spots closely located in the Dapeng Peninsula is calculated. The results not only provide a reliable “A-rating” system to gauge the popularity of different scenic spots, but also contribute an innovative measuring model to support scenic spots planning and policy making in the regional context.

Keywords: tourist preference; measuring model; tourism destinations; scenic spots planning; social media data (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2017
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (5)

Downloads: (external link)
https://www.mdpi.com/2071-1050/10/1/43/pdf (application/pdf)
https://www.mdpi.com/2071-1050/10/1/43/ (text/html)

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:gam:jsusta:v:10:y:2017:i:1:p:43-:d:124420

Access Statistics for this article

Sustainability is currently edited by Ms. Alexandra Wu

More articles in Sustainability from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().