Reviews about tourism products in online environments are an important data source for tourism businesses, destination managements and tourists. Tourist reviews online are completely unbiased reviews created voluntarily by tourists. Therefore, important feedback is provided for tourism businesses and destinations in the evaluation of tourism products. Collecting and analyzing tourist comments and transforming them into strategic information will create an important competitive power. Sentiment analysis, which is a sub-field of text mining, is a field of study that analyzes people's ideas and thoughts about tourism products and services from text-based comments. Sentiment analysis can be applied at the document level, sentence level and aspect-based sentiment levels. In this chapter, sentiment analysis methods are examined by using data collected from online platforms where tourism products are evaluated. Analyzing online tourist reviews using text mining methods will provide important opportunities for stakeholders in the tourism sector. These opportunities can be explained in terms of destination managements, tourism businesses and tourists. Understanding and interpreting the destination for destination management will thus provide opportunities for the creation of the brand value and image of the destination. It will also make an important contribution to the determination of tourist needs in the destination and meeting these needs. In terms of tourism businesses, evaluating the products and services they offer to tourists will create an opportunity to manage customer relations by discovering the negative and positive aspects. In addition, tourism businesses will have the opportunity to develop or improve their products and services in order to gain product and price advantage by evaluating their competitors. Tourists will be able to use it to make better travel plans.
Ozen, I. A. (2021). Tourism products and sentiment analysis. In C. Cobanoglu, S. Dogan, K. Berezina, & G. Collins (Eds.), Advances in Hospitality and Tourism Information Technology (pp. 1–44). USF M3 Publishing. https://www.doi.org/10.5038/9781732127586
Creative Commons License
This work is licensed under a Creative Commons Attribution-Noncommercial 4.0 License