Enhancing automatic blog classification using concept-category vectorization
|Enhancing automatic blog classification using concept-category vectorization|
|Author(s)||Ayyasamy R.K., Alhashmi S.M., Eu-Gene S., Tahayna B.|
|Editor(s)||Wang Y.Li T.|
|Published in||Advances in Intelligent and Soft Computing|
|Keyword(s)||Blog classification, Weighting Scheme, Wikipedia (Extra: Blogging, Category systems, Classification framework, Two stage, User-generated content, Vectorization, Weighting scheme, Wikipedia, Intelligent systems, Knowledge engineering, Blogs)|
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Enhancing automatic blog classification using concept-category vectorization is a 2011 conference paper written in English by Ayyasamy R.K., Alhashmi S.M., Eu-Gene S., Tahayna B. and published in Advances in Intelligent and Soft Computing.
Blogging has gained popularity in recent years. Blog, a user generated content is a rich source of information and many research are conducted in finding ways to classify blogs. In this paper, we present the solution for automatic blog classification through our new framework using Wikipedia's category system. Our framework consists of two stages: The first stage is to find the meaningful terms from blogposts to a unique concept as well as disambiguate the terms belonging to more than one concept. The second stage is to determine the categories to which these found concepts appertain. Our Wikipedia based blog classification framework categorizes blog into topic based content for blog directories to perform future browsing and retrieval. Experimental results confirm that proposed framework categorizes blogposts effectively and efficiently.
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