A Study on the Perception and Value Evaluation of Regional Tourism Brands Using Big Data

다국어 초록 (Multilingual Abstract)

In this study, big data on text containing tourism brands were collected and analyzed targeting comments and web pages that were actually searched on various online media such as portals, SNS and YouTube using tourism brands as search terms. To this end, text mining analysis was performed on online big data for the Boryeong Mud Festival. Textom, a processing program for big data collection and analysis, was used as the analysis tool. In the analysis results, keywords related to the Boryeong Mud Festival shown in big data were mainly terms related to global and tourism products. Therefore, in the case of the Boryeong Mud Festival as a tourism brand, its value as an ecotourism and stay-type tourism brand was found to be very large in terms of experiential tourism resources. Matrix analysis shows that the Boryeong Mud Festival has high tourism brand value as an online festival, international forum and international marine city in Asia. In the results of the topic modelling analysis, topics related to the use of ecotourism resources such as air bounce, large mud bath and experience were extracted as keywords related to the event of the Boryeong Mud Festival. In addition, topics related to economic value as residence-type tourism resources were extracted in terms of foreigners entry and stay. Topics related to the use of ecological tourism resources at the Boryeong Mud Festival were extracted as well. This shows that the Boryeong Mud Festival can generate profits through effective online marketing because the product value of the brand of ecotourism resources is very large. These results will provide an opportunity to understand the structure of the product value of tourism brands and users perceptions of them through keyword analysis of text mining and to develop them as tourism resources using major topics.

목차 (Table of Contents)

• I. Introduction II. Theoretical Background III. Method IV. Analysis Results V. Conclusion