Understanding destination brand love using machine learning and content analysis method

ABSTRACT

This study aims to apply the concept of brand love in tourist destinations in order to identify the core-elements that could have influential impacts on generating destination brand love. This study has been carried out using a mixed-method of machine learning and content analysis. We have discovered that the topics have been generated for historical landmarks and destinations by analysing the visitors’ online reviews are architecture, historical sites, tradition and shrine places, which could be similar to other tourist historical destinations in a different part of the world. However, this study has the potential to be a model for other researches related to different destinations with possible different topics that emerged. Our study contributes by providing both researchers and managers a novel method to understand what attributes of destination brand love they need to posit more emphasize to attract more visitors based on the destination type.

KEYWORDS:

• Destination brand love
• online reviews
• latent Dirichlet allocation (LDA)
• support vector machine (SVM)
• content analysis

Disclosure statement

No potential conflict of interest was reported by the author(s).