关键词: 用户生成内容, Flickr图片, 深度学习, 旅游大数据, 目的地形象, 北京

Abstract:

With the rapid development of the Internet, increasingly more tourists use social media to share travel experiences. User generated content (UGC) has become an important source of information for potential tourists, affecting their perception of tourism destination image (TDI) and tourism decision making. Photos, as one of the carrieres of UGC, is an important tool for the spread of TDI across cultures. However, most of the previous photo-based TDI research mainly adopt content or semantic analysis by human, and the number of samples analyzed was limited. When facing large-scale UGC photo collections, more automated analysis methods are needed to improve efficiency. This article introduces the deep learning theory of computer science into TDI for the first time, taking the pictures released by tourists (from Hong Kong, Macao, and Taiwan of China; the United States; the United Kingdom) as the research samples and using machine analysis from millions of destination related photos to represent content and construct the correspondence between destination cognitive and affective images. With regard to the cognitive image, inbound tourists are more concerned about nature and architecture, but they pay different attention to culture, art, people, food and other aspects. Tourists from Hong Kong, Macao, and Taiwan of China are mostly interested in cultural relics, entertainment activities, and food. UK tourists pay attention to facilities and urban environment. US tourists are more likely to photograph people. With regard to the affective image, “exciting” and “pleasant” are the most significant affective elements for inbound tourists. Hong Kong, Macao, Taiwan of China, and US tourists show some sign of sleepiness, and UK tourists show more sign of distress. According to these results, targeted marketing for the main inbound tourist source markets is needed. For tourists from Hong Kong, Macao, and Taiwan of China, it’s essential to increase the cultural content of Beijing, diversify food and Beijing-style entertainment activities. For UK tourists, well-equipped facilities, diverse urban environments, and distinctive buildings are key marketing contents. Natural scenery and life of people are important symbols for attracting US tourists. In summary, this study used computer deep learning methods to analyze large-scale photo data sets, converting pictorial images into text to extract cognitive and affective images. It has both methodological and managerial implications.

Key words: user generated content, Flickr photos, deep learning, tourism big data, destination image, Beijing