Internet public opinion hotspot detection and analysis based on Kmeans and SVM algorithm
Rapid progress of network arouses much attention on Internet public opinion, it is important to grasp the internet public opinion in time and understand the trends of their opinion correctly. Text mining plays a fundamental role in categorization and monitoring of internet public opinion, but internet public opinion is much more difficult than pure-text process because of their semi-structured characteristic. To address this issue, we propose a model for internet public opinion hotspot detection and analysis. Due to the text format of internet public opinion, we introduce the traditional vector space model (VSM) to express them, and then use Kmeans algorithm to perform text clustering on a corpus collected from some news website, and use SVM classifier to perform text categorization for new text opinion analysis, the result of the experiment shows that the efficiency and effectiveness of such method.
Internet public opinion hotspot detection text categorization vector space model SVM Kmeans clustering
Hong Liu
College of Computer and Information Engineering, Zhejiang Gongshang University HangZhou, China
国际会议
西安
英文
257-261
2010-08-07（万方平台首次上网日期，不代表论文的发表时间）