期刊名称：International Journal of Advanced Research in Computer Engineering & Technology (IJARCET)
印刷版ISSN：2278-1323
出版年度：2014
卷号：3
期号：10
页码：3461-3466
出版社：Shri Pannalal Research Institute of Technolgy
摘要：The goal of FoCUS is to crawl relevant forum content from the web with minimal overhead. The forums have different layouts and are powered by different forum software packages; they always have similar implicit navigation paths connected by specific URL types to lead users from entry pages to thread pages. The web forum crawling problem is reduced to a URL-type recognition problem and classifies them as Index Page, Thread Page and Page-Flipping page. To address the scalab ility issue, the research proposes an edge-centric clustering scheme is to extract sparse social dimensions approach can efficiently handle n etworks of millions of actors while representing a comparable prediction performance to other non-scalable methods. In addition, the research includes a new concep t called sentiment analysis which transforms the cases into a standard model of features and classes. This is developed in two stages: emotional polarity computation and integrated sentiment analysis based on K -means clustering. The proposed unsupervised text-mining approach is used to group the forums into various clusters, with the center of each representing a hotspot forum within the current time span. As a result, the behavior of individuals is collected through their posts in a forum and then they are classified as positive/negative posts. The positive and negative value is assign to each word and to classify the word in the document.
关键词：Entry index thread path; Forum crawling; Page classification; Page type; URL pattern learning; ; URL type.
