The increasing popularity of tourist generated content has created abundant opportunities for people to understand the opinions and experiences of prior tourists. However, till now no framework has been presented to automatically discover useful patterns from structured tourism blogs. In this paper, we present a method to mine the tourism information such as frequented spots and popular travel service within a given travel destination. These information can help us understand the travel destination and enable the website to recommend interesting travel spots. First, we introduce the method for compact pattern mining and sequential pattern mining. Then we propose a framework to analyze the structured tourism blogs. Particularly, sequential pattern mining was conducted to discover the frequented spots and their correlations. Then compact pattern mining was conducted to detect the spot associated travel service like shopping, etc. Finally, the experimental results based on an online tourism blog dataset (in Chinese) illustrate advantages of the proposed method.
Guo, Lei; Li, Ziru; and Sun, Wenjun, "Understanding Travel Destinations From Structured Tourism Blogs" (2015). WHICEB 2015 Proceedings. 80.