| With the rapid development of tourism market,the demand for intelligent travel services has been expected to increase remarkably.The prevalence of the Internet enables everyone to easily access travel related information from various websites.However,the sustained growth of travel data on the web may be overwhelming for tourists when selecting tourist attractions that specific to their personalized requirements.Meanwhile,tour operators need to present customized tourist attractions for potential tourists so as to survive in competitive market and make more profit.Therefore,it is highly desirable to produce a precise analysis and summary of online attraction information,with the objective of providing decision support for both tourists and tour operators.To this end,many probabilistic topic models have been proposed for feature extraction of tourist attraction.However,existing state-of-the-art probabilistic topic models overlook the common sense that tourist attractions tend to have distinct characteristics with respect to specfic seasonal context.In fact,the attraction textual data is significantly different from other common documents since the content of an attraction description text often reveal a strong seasonal pattern.Hence,it is necessary to develop a suitable approach to address the unique characteristics of the attraction textual data and precisely extract the topic features of tourist attractions with consideration of seasonal contextual information.However,to the best of our knowledge,so far no research has focused on this topic.In this article,we contribute the innovative idea of using seasonal contextual information to refine the characteristics of tourist attractions.Along this line,we first propose STLDA,a season topic model based on latent Dirichlet allocation which can capture meaningful topics corresponding to various seasonal contexts for each attraction from a collection of attraction description documents.Then,an inference algorithm using Gibbs sampling is put forward to learn the posterior distributions and model parameters of our proposed model.In order to verify the effectiveness of STLDA model,we present a detailed experimental study using collected real-world textual data of tourist attractions.The experimental analysis results show that the superiority of STLDA over the basic LDA model in detecting the season-dependent topics and giving a representative and comprehensive summarization related to each tourist attraction.Mining tourist highlights of attractions and improving the public awareness for travel resources in different seasons based on this study undoubtedly have great practical significance in promoting the development of local tourism economy. |
