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Understanding destination brand love using machine learning and content analysis method

Author

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  • Nader Seyyedamiri
  • Ali Hamedanian Pour
  • Ehsan Zaeri
  • Alireza Nazarian

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.

Suggested Citation

  • Nader Seyyedamiri & Ali Hamedanian Pour & Ehsan Zaeri & Alireza Nazarian, 2022. "Understanding destination brand love using machine learning and content analysis method," Current Issues in Tourism, Taylor & Francis Journals, vol. 25(9), pages 1451-1466, May.
    • Handle: RePEc:taf:rcitxx:v:25:y:2022:i:9:p:1451-1466
    DOI: 10.1080/13683500.2021.1924634
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    Cited by:

    1. Soojung Kim & Jinsoo Hwang, 2023. "Airline CSR and Quality Attributes as Driving Forces of Passengers’ Brand Love: Comparing Full-Service Carriers with Low-Cost Carriers," Sustainability, MDPI, vol. 15(9), pages 1-14, April.

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