vioft2nntf2t|tblJournal|Abstract_paper|0xf4ffe67c22000000308d050001000400 Users of Online Social Network (OSN) communicate with each other, exchange information and spread rapidly influencing others in the network for taking various decisions. Blog sites allow their users to create and publish thoughts on various topics of their interest in the form of blogs/blog documents, catching the attention and letting readers to perform various activities on them. Based on the content of the blog documents posted by the user, they become popular. In this work, a novel method to profile Top Most Influential Blogger (TMIB) is proposed based on content analysis. Content of blog documents of bloggers under consideration in the blog network are compared and analyzed. Term Frequency and Inverse Document Frequency (TF-IDF) of blog documents under consideration are obtained and their Cosine Similarity score is computed. Synonyms are substituted against those unmatched keywords if the Cosine Similarity score so computed is below the threshold and an improved Cosine Similarity score of those documents under consideration is obtained. Computing the Influence Score after Synonym substitution (ISaS) of those bloggers under conflict, the top most influential blogger is profiled. The simulation results demonstrate that the proposed Profiling Top Most Influential Blogger using Synonym Substitution (PTMIBSS) algorithm is adequately accurate in determining the top most influential blogger at any instant of time considered.
G U Vasanthakumar, R Priyanka, K C Vanitha Raj, S Bhavani, B R Asha Rani, P. Deepa Shenoy, K.R .Venugopal
University Visvesvaraya College of Engineering, Bangalore University, India
Blog Document; Content Analysis; Cosine Similarity Score; Influential Blogger; Profiling
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| Published By :
ICTACT
Published In :
ICTACT Journal on Soft Computing
( Volume: 7 , Issue: 2 , Pages: 1408-1420 )
Date of Publication :
January 2017
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675
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