Finding Information in Blogs: Efficient Keyword Extraction in Blog Mining

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题名:	Finding Information in Blogs: Efficient Keyword Extraction in Blog Mining
作者:	陳宜惠;CHEN, YI-HUI;呂瑞麟;Lu, Eric Jui-Lin;蔡孟芳;Tsai, Meng Fang
贡献者:	資訊多媒體應用學系
关键词:	Blog mining;User intention;Co-keyword;Blog Connect;Full-text keyword retrieval procedure
日期:	2014-02
上传时间:	2014-06-05 04:21:41 (UTC+0)
摘要:	Readers are becoming accustomed to obtaining useful and reliable information from bloggers. To make access to the vastly increasing resource of blogs more effective, clustering is useful. Results of the literature review suggest that using linking information, keywords, or tags/categories to calculate similarity is critical for clustering. Keywords are commonly retrieved from the full text, which can be a time-consuming task if multiple articles must be processed. For tags/categories, there is also a problem of ambiguity; that is, different bloggers may define tags/categories of identical content differently. Keywords are important not only to reflect the theme of an article through blog readers’ perspectives but also to accurately match users’ intentions. In this paper, a tracing code is embedded in Blog Connect, a newly developed platform, to collect the keywords queried by readers and then select candidate keywords as co-keywords. The experiments show positive data to confirm that co-keywords can act as a quick path to an article. In addition, co-keyword generation can reduce the complexity and redundancy of full-text keyword retrieval procedures and satisfy blog readers’ intentions.
關聯:	EXPERT SYSTEMS WITH APPLICATIONS,41(2),663–670.
显示于类别:	[國際企業學系] 期刊論文

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