TUMS: twitter-based user modeling service
作者： Ke Tao , Fabian Abel , Qi Gao , Geert-Jan Houben
DOI: 10.1007/978-3-642-25953-1_22
关键词:
摘要: Twitter is today's most popular micro-blogging service on the Social Web. As people discuss various fresh topics, messages (tweets) can tell much about current interests and concerns of a user. In this paper, we introduce TUMS, Twitter-based User Modeling Service, that infers semantic user profiles from post Twitter. It features topic detection entity extraction for tweets allows further enrichment by linking to news articles describe context tweets. TUMS made publicly available as Web application. end-users overview in structured way them see which topics or entities was interested at specific point time. Furthermore, it provides RDF format applications incorporate these order adapt their functionality via: http://wis.ewi.tudelft.nl/tums/
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