Political Leaning Categorization by Exploring Subjectivities in Political Blogs

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 subjectivity 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 opinion expressions from subjective sentences, we are able to reveal opinions that are characteristic of a specific political orientation to some extent.