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Exploiting Subjectivity Analysis in Blogs to Improve Political Leaning Categorization
 

Summary: Exploiting Subjectivity Analysis in Blogs to Improve
Political Leaning Categorization
Maojin Jiang and Shlomo Argamon
Linguistic Cognition Lab, Department of Computer Science, Illinois Institute of Technology
10 West 31st Street, Chicago, IL 60616 USA
{jianmao, argamon}@iit.edu
ABSTRACT
In this paper, we address a relatively new and interesting
text categorization problem: classify a political blog as ei-
ther liberal or conservative, based on its political leaning.
Our subjectivity analysis based method is twofold: 1) we
identify subjective sentences that contain at least two strong
subjective clues based on the General Inquirer dictionary;
2) from subjective sentences identified, we extract opinion
expressions and other features to build political leaning clas-
sifiers. Experimental results with a political blog corpus we
built show that by using features from subjective sentences
can significantly improve the classification performance. In
addition, by extracting opinion expressions from subjective
sentences, we are able to reveal opinions that are character-

  

Source: Argamon, Shlomo - Department of Computer Science, Illinois Institute of Technology

 

Collections: Computer Technologies and Information Sciences