The following information was submitted:
Transactions: WSEAS TRANSACTIONS ON COMPUTERS
Transactions ID Number: 54-175
Full Name: Subramaniyaswamy Vairavasundaram
Position: Associate Professor
Age: ON
Sex: Male
Address: Associate Professor,Dept. of CSE,Selvam College of Technology,Salem Road,Namakkal,Tamil Nadu,India
Country: INDIA
Tel: 9865968789
Tel prefix: 91
Fax:
E-mail address: vsubramaniyaswamy@gmail.com
Other E-mails: vs_swamy1980@yahoo.co.in
Title of the Paper: An improved approach for Topic Ontology Based Categorization of Blogs Using Support Vector Machine
Authors as they appear in the Paper: Mr.V.Subramaniyaswamy and Dr. S.Chenthur Pandian
Email addresses of all the authors: vsubramaniyaswamy@gmail.com,chenthur@rediffmail.com
Number of paper pages: 13
Abstract: Nowadays blogs are widely used by internet users to circulate thoughts and information concurrently. Information collection from the blogs is still one of the important and complicated problems. Mainly the blogs assist the variety of interesting information. Tags play a vital role in annotating and organizing the blogs in a web. Blog contents are associated with a set of predefined topic ontology keywords. This paper proposes categorization of blogs to facilitate easy identification of user expected topic from the massive collection of blogs. Tags, page contents are collected as inputs from the blogs and the blogs are categorized using Support Vector Machine (SVM) algorithm. Most frequent occurrences of topic ontological keywords are used to train the classifier. This approach describes the empirical evaluation of blog categorization using SVM. The performance is evaluated for precision and recall for blog categorization using SVM with Naïve Bayes algorithm. It is !
proved that SVM improves the classification accuracy than Naïve Bayes algorithm. The experiments showed that tags and contents are very efficient for the blog categorization.
Keywords: Tags, Page content, Topic ontology, Blogs, Categorization, SVM.
EXTENSION of the file: .pdf
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