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  • HSE University
  • Student Theses
  • Tourist Brand Perception Evaluation with Content Analysis Utilization of the Internet Users' Reviews: the Case of Tbilisi

Student
Title
Supervisor
Faculty
Educational Programme
Final Grade
Year of Graduation
Mariia Sapozhnikova
Tourist Brand Perception Evaluation with Content Analysis Utilization of the Internet Users' Reviews: the Case of Tbilisi
2016
Master's thesis is devoted to the creation of the algorithm of tourist brand perception evaluation with content analysis utilization of the internet users' reviews.

The relevance of this study is confirmed by the fact that the reviews on the Internet affect tourists’ choices and behavior. Therefore, it is important for tourist destination to evaluate how tourists perceive their brand correctly and to use it to improve their results, as well as to efficiently and effectively allocate resources.

The first chapter describes the basic concepts of the study, their evolution, are prerequisites. The results of theoretical and practical research on the topic are generalized. The methods of evaluating the brand the city as a tourist destination are considered. In the second chapter describes the methodology of the study is described - namely, content analysis and its application in the evaluation of brand of the city as a tourist destination. In the third chapter, we have made the estimation algorithm of perception of tourist destination brand with the help of content analysis and explored the peculiarities of perception of the tourism brand of Tbilisi.

During the study, based on the synthesis of previous research on this topic, the algorithm for estimating the perception of a brand destination with the help of content analysis of reviews on the Internet was created. The algorithm was tested on the example of Tbilisi. 200 ravelers' reviews were analyzed. The results suggest that tourists perceive the city as a whole is positive, but not all of the desired characteristics, which laid the creators of the brand in its identity, reflected in the reviews.

The resulting algorithm can be used for destinations that have formed the city's official tourism brand.

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