期刊名称：International Journal of Computer Science and Network
印刷版ISSN：2277-5420
出版年度：2016
卷号：5
期号：2
页码：246-250
出版社：IJCSN publisher
摘要：Recently, a new form of blogging has emergedknown as microblogging. Microblogging is a simplified formof blogging where entries are restricted in length, typically toaround 140 characters or less. Microblog usage has growndramatically recently thanks in part to Twitter, the leadingprovider of microblogs, and the integration of microbloggingservices. In this dissertation, attempt to address some of theopportunities and challenges of automatically processingmicroblogging data by considering two specific problems.First, an automatically Keyword Strap classifier that classifya single Twitter post into a set of high-level categories using aNaïve Bayes classifier. While such tasks have been performedbefore using traditional blogs, no such research exists to ourknowledge of applying this technique to microblogging data.Our research indicates that even though an average Twitterpost is only 11 words in length they can be categorized intoone of ten categories with an Fl-measure up to 78%. Secondly,automatically summarize a large number of Twitter messagesand calculate happy index of user.
