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of that unrest on Phuket's tourism environment. It is proposed that this analysis can provide measurable insights through summarization, keyword analysis and clustering. We measure sentiment using a binary choice keyword algorithm. A multi-knowledge based approach is proposed using, Self-Organizing Maps along with
Recent years have witnessed the increased application of AI technologies to real-world e-commerce challenges. This article presents a brief overview of representative work by Chinese researchers, covering topics such as multiagent decision making, keyword advertising, social networks, recommender systems, information
their personal file system by leveraging semantic relationships available on the Web. More specifically, JabberWocky is using keyword/resource associations of social bookmarking web sites as a basis for recommending keywords for files. We chose social bookmarking web sites because of their popularity and because the
measure sentiment using a binary choice keyword algorithm and a multi-knowledge based approach is proposed using, Self-Organizing Maps and tourism domain knowledge in order to model sentiment. We develop a visual model to express this taxonomy of sentiment vocabulary and then apply this model to maximums and minimums in the
Folksonomy systems enable users to participate in the Web content creation process by annotating (tagging) resources with freely chosen keywords. Still, it is an open issue how to exploit this user-created content, and how to process and use these emergent semantics effectively. We investigate how the context of Web
) information content due to occurrence of a property with respect to all the properties in a description base ii) unpredictability of an association due to participation of its properties in multiple domains iii) the extent of match between user specified keywords and properties and iv) the popularity of nodes involved in a
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