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| THE COGNITIVE HOTSPOTS AND RELEVANCE OF INBOUND TOURISM IN XINJIANG |
| WANG Lei-lei, ZHAO Zhen-bin, LI Juan |
| College of Tourism and Environment, Shaanxi Normal University, Xi'an 710062, China |
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Abstract Related studies on tourism cognitive often come from deep study of the tourism destination image, but the existing studies on large data using is not enough, and focuses on the rule of tourism cognitive phenomenon, which seldom based on the relationship of independent words. The deep analysis of the cognitive relevance is helpful to explain the reason of cognitive and mechanism, it is a good way to understand the tourism cognitive deeply, it is also the direction of the further development of network content analysis. Taking the English blogs on Xinjiang tourism written on major online communities by inbound visitors as samples, and aided by the software of ANTCONC and UCINET, this article uses co-occurrence analysis and social network analysis to study on inbound visitors' cognitive hot issues and cognitive relevance. As a result:Inbound visitors' cognition on tourism has a three dimensional relevance structure model comprising space, emotion and action; The reveal of the cognition relevance will help improving the efficiency of Xinjiang tourism web recommendation system; Similar landscape have more geographical cognition relevance. Urumqi is the largest city in cognitive hotspots, Kanas is one of the most influential tourist scenic spot. Most inbound tourists are given a positive assessment. Hiking and car is the main way to travel. In the end, some suggestions are given to help the development of Xinjiang. The co-occurrence of co-occurrence includes two aspects, one is the core contents in the co-occurrence between keywords, the second is the core keywords and its specific radius of co-occurrence buffer between vocabulary. From a practical sense, cognitive association analysis can provide the basis for building network of tourism marketing among the recommended system. The current recommendation technology network just uses the recommendation algorithm based on association rules to identify user needs and preferences.
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Received: 20 March 2014
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2014, Vol. 29 
