인천 관광지의 속성별 평가를 위한 딥러닝 기반 감성분석

Abstract

This study applied the IPA(importance-performance analysis) methodology to unstructured text data to evaluate tourists' satisfaction levels with Incheon attractions. Focusing on attractions within the maintenance and improvement-needed areas identified by IPA, it extracted key positive and negative keywords and sentences directly from tourists' opinions. By applying a text-based approach to evolving IPA research, this exploratory study assessed the concrete satisfaction and dissatisfaction elements felt by tourists. The study limited its scope to 15 Incheon attractions and analyzed 114,007 sentences from Naver blogs written by tourists. Satisfaction levels were derived through deep learning sentiment analysis of the text, while the importance of each attraction was determined by the TF-IDF(term frequency-inverse document frequency) ratio of the attraction names in the text categorized by attributes such as attraction, transportation, activities, and food/drink. Key positive and negative keywords and sentences were extracted using the TextRank algorithm, focusing on attractions in the maintenance and improvementneeded areas. The findings reveal that experiential elements of attractions act as differentiating factors, while their absence can provoke negative reactions from tourists. Moreover, content reflecting local culture and history, the development of family-friendly experiential attractions, parking space expansion, and food service improvements emerged as crucial elements for attraction development.