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Recently, one of the most popular applications on the Internet is the social networking, such as, Facebook and Twitter. Finding resources using recent Peer-to-Peer (P2P) schemes in a social network has limits, such as distance problem between peers etc. So, in this paper, we present an efficient social P2P management
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
In this paper we mine over 80 million twitter micro logs in order to explore whether data from this social media initiative can be used to identify sentiment about tourism and Thailand amid the unrest in that country during the early part of 2010 and further whether analysis of tweets can be used to discern the effect
EventGraphs are social media network diagrams of conversations related to events, such as conferences. Many conferences now communicate a common "hashtag" or keyword to identify messages related to the event. EventGraphs help make sense of the collections of connections that form when people follow, reply or mention
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