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Political Leaning Categorization by Exploring Subjectivities in Political Blogs
 

Summary: Political Leaning Categorization by Exploring Subjectivities in
Political Blogs
Maojin Jiang and Shlomo Argamon
Linguistic Cognition Lab, Department of Computer Science, Illinois Institute of Technology
10 West 31st Street, Chicago, IL 60616 USA
Abstract--This paper addresses a relatively new text
categorization problem: classifying a political blog as either
`liberal' or `conservative', based on its political leaning.
Instead of simply using "Bag of Words" features (BoW) as
in previous work, we have explored subjectivity manifested
in blogs and used subjectivity information thus found to help
build political leaning classifiers. Specifically, our subjec-
tivity based approach is two fold: 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 BoW
features to build political leaning classifiers. Experiments
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

  

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

 

Collections: Computer Technologies and Information Sciences