Exploring in the weblog space by detecting informative and affective articles

Weblogs have become a prevalent source of information for people to express themselves. In general, there are two genres of contents in weblogs. The first kind is about the webloggers' personal feelings, thoughts or emotions. We call this kind of weblogs affective articles. The second kind of weblogs is about technologies and different kinds of informative news. In this paper, we present a machine learning method for classifying informative and affective articles among weblogs. We consider this problem as a binary classification problem. By using machine learning approaches, we achieve about 92% on information retrieval

• Type: Conference
• Year: 2007
• Where: WWW
• Authors: Xiaochuan Ni, Gui-Rong Xue, Xiao Ling, Yong Yu, Qiang Yang
• Keywords: Binary Classification Problem, Internet Technology, Machine Learning Approaches, Weblogs Affective Articles, WWW 2007