Customer Segmentation and Classification from blogs by Using Data Mining: an Example of VoIP Phone

Blogs have been considered the 4 th Internet application which can cause radical change in the world, after E-mail, Instant Message, and Bulletin Board System (BBS). Lots of Internet users heavily rely on them to express their emotions and personal comments on whatever topics interest them. Nowadays, blogs have become the popular media and could been viewed as new marketing channels. Depending on the blog search engine, Technorati, we can track about 94 million blogs in August 2007. It also reported that a whole new blog is created every 7.4 seconds and 275 thousands blogs are updated daily. These figures can be used to illustrate the reason why more and more companies attempt to discover useful knowledge from that huge amount of blogs for business purposes. Therefore, blog mining could be a new trend of web mining. The major objective of this study is to present a structure which includes unsupervised (Self-organizing Map) and supervised learning methods (Back-Propagation Neural Networks, Decision Tree, and Support Vector Machines) for extracting knowledge from blogs, namely Blog Mining (BM) model. Moreover, a real case regarding VoIP (Voice over Internet Protocol) phone products is provided to demonstrate the effectiveness of the proposed method.