Track: Industrial Practice and Experience
Paper Title:
Exploring in the Weblog Space by Detecting Informative and Affective Articles
Authors:
Abstract:
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.
