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Refereed Papers

Track: Industrial Practice and Experience

Paper Title:
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 weblogger's personal feelings, thoughts or emotions. We call this kind of weblogs affective articles. A 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 92% on information retrieval performance measures including precision, recall and F1. We set up three studies on the applications of above classification approach in both research and industrial fields. We use the above classification approach to improve the performance of classification of emotions from weblog articles. We also develop an intent-driven weblog-search engine based on the classification techniques to improve the satisfaction of web users. Finally, we use above classification approach to search for weblogs with a great deal of informative articles.

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