관광 목적지 이미지에 영향을 미치는 속성에 대한 IPA 분석:텍스트 마이닝의 활용

초록

In the rapidly evolving landscape of the modern tourism industry, the cultivation of a favorable image for a tourist destination emerges as a pivotal element for success. Historically, research into tourist perceptions has predominantly relied on methodologies imbued with subjective biases, such as surveys and interviews. This research introduces a novel and streamlined methodological approach, integrating text mining and importance-performance analysis (IPA) for the objective evaluation of online reviews, thereby assessing the attributes that shape the image of a tourist destination. The methodological framework entails the creation of an attribute-specific word dictionary, the execution of frequency and sentiment analyses, and the application of IPA modeling through Min-max scaling. This approach facilitates the identification of the significance of each attribute through the frequency analysis of pertinent keywords, and the assessment of satisfaction levels pertaining to each attribute via sentiment analysis of online reviews. These analytical insights are instrumental in pinpointing areas necessitating enhancement and in delineating the strengths and weaknesses of a tourist locale, by elucidating the interplay between the importance and satisfaction associated with each attribute within the IPA model. An empirical application of this methodology is demonstrated through the analysis of 6,133 reviews from Tripadvisor.com, pertaining to Myeong-dong, a prominent shopping district in Seoul. This analysis utilized the IPA model to graphically represent the perceived importance of various attributes in the formation of a tourist destination's image and the degree of satisfaction associated with each. The implications of this study are manifold, offering valuable insights for decision-makers in government and related sectors to augment the international standing of tourist destinations by refining tourist perceptions.

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