Exploiting subjectivity analysis in blogs to improve political leaning categorization

ABSTRACT

In this paper, we address a relatively new and interesting text categorization problem: classify a political blog as either 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 classifiers. 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 characteristic of a specific political leaning to some extent.

REFERENCES

• 1: M. Jiang and S. Argamon. Finding political blogs and their political leanings. In Text Mining 2008, Workshop at the SIAM International Conference on Data Mining, April 2008.

INDEX TERMS

Primary Classification:
  H. Information Systems
  H.3 INFORMATION STORAGE AND RETRIEVAL
      H.3.1 Content Analysis and Indexing
          Subjects: Linguistic processing

General Terms:
Algorithms, Experimentation

Keywords:
political leaning categorization, subjectivity analysis