머신러닝 기반 색채적 특성 분석 및 감성 분류에 관한 연구

Abstract

This study focuses on developing a methodology for classifying tourist destination images based on their color characteristics and emotional categories using machine learning algorithms. By analyzing user-generated content collected through digital media and smartphones, particularly tourist destination images from Instagram, the research examines how the initial impressions and imagery of tourist destinations impact tourists' emotions. Color and text data were analyzed using machine learning algorithms and the BERT language model to interpret the color characteristics and emotional messages conveyed by these images. The research demonstrates that analyzing and classifying the color characteristics and emotions of tourist destination images significantly influence tourists' choices and emotions. It shows that considering these two factors in an interconnected manner, rather than in isolation, enhances their utility. Specifically, color characteristics are crucial elements that can improve the attractiveness of tourist destination images. This study reveals that these images can significantly affect tourists' emotions and experiences, offering new insights and effective strategies for optimizing the tourism experience. Moreover, by systematically analyzing and utilizing the data, it is possible to provide customized suggestions that reflect the preferred images of tourist destinations and user tendencies. This research provides critical insights into image management and marketing strategies for tourist destinations, helping to attract tourists' interest and create positive tourism experiences. Ultimately, the study aims to deepen the understanding of the tourism industry through the color characteristics and emotional classification of tourist destination images, making significant contributions to the enhancement of image management and marketing strategies for tourist destinations.