基于网络社交平台数据挖掘的出境游客满意度研究-旅游管理专业论文.docx

万方数据 万方数据 Abstract Tourist satisfaction has always been a hot issue in tourism research. Whether domestically or abroad, there have been plenty of valuable researches. Mature tourist satisfaction theory system was formed, and established solid theoretic foundation. However, related researches were mostly done without quantitative methods based on massive data. This is because of two difficulties: first, the limitation of questionnaires being the sole evaluation method, second, the collection of data. This paper presented a method to approach tourist satisfaction analysis using state-of-the-art techniques in computer science and natural language processing (data-mining and sentiment analysis), combined with content analysis methodology, to efficiently gather data, and to objectively analyze them, a method in which credibility and adaptability can be ensured. The result of this page has unique value among researches in tourists who are going abroad. Especially, the successful use of computer technologies such as sentiment analysis has broadened the perspective of satisfaction analysis applications. The target of this research is Chinese tourists in six destinations (namely Hong Kong, Macau, Korea, Taiwan, Thailand, and Japan). Using a computer program, a total of 828,113 entries of Weibo are gathered from Sina Weibo Places. Using Maximum Entropy Modeling (Machine Learning in Computer Science), 12,000 random entries were chosen for manual tagging and automated language feature extraction. They serve as training and evaluation data, to produce a classification model that is capable of sentiment analysis on such data. Based on this model, all entries were automatically classified as positive, neutral, or negative. The results of classification served as the input of a Cost versus Gain model, which eliminated the factor of consumer pricing and was capable of comparing tourist satisfaction between different destinations (or comparing price-performance ratio in terms