Advertisement

Quantitative Content Analysis of Chinese Texts?: A Methodological Note

  • Yu-Wen Chen
Research Article

Abstract

This paper discusses the quantitative measurement of Chinese texts using hand-coded analysis, Yoshikoder and Wordscores. The paper compares the advantages and disadvantages of each method using the example of the 2008 melamine-tainted milk scandal in China. The policy positions estimated by Yoshikoder are not too different from those using hand-coded analysis. Yoshikoder outperforms Wordscores, but with substantial human intervention.

Keywords

Quantitative Content Analysis Computer-Assisted Content Analysis (CCA) Yoshikoder Wordscores Melamine-Tainted Milk Scandal 

Notes

Acknowledgements

This paper was completed during my postdoctoral stay at the Chair of International Politics at the University of Konstanz in Germany and at the Institute for Human Security at La Trobe University in Australia. For financial support for my project on “The Rise of Group Interests and Evidences of Interest Articulation in China” (Taiwan NSC98-2917-I-564-147), I would like to thank Taiwan’s National Science Council. John James Kennedy has commented thoroughly on my earlier draft. I wish to thank him for his assistance.

References

  1. 1.
    Benoit, K., and M. Laver. 2003. Estimating Irish party policy positions using computer wordscoring: The 2002 election-a research note. Irish Political Studies 18(1): 97–107.CrossRefGoogle Scholar
  2. 2.
    Carlson, J.M., and M.S. Hyde. 2003. Doing empirical political research. Boston and New York: Houghton Mifflin Company.Google Scholar
  3. 3.
    Klemmensen, R., S.B. Hobolt, and M.E. Hansen. 2007. Estimating policy positions using political texts: An evaluation of the wordscores approach. Electoral Studies 26(4): 746–755.CrossRefGoogle Scholar
  4. 4.
    Klüver, H. 2009. Measuring interest group influence using quantitative text analysis. European Union Politics 10(4): 535–549.CrossRefGoogle Scholar
  5. 5.
    Laver, M., K. Benoit, and J. Garry. 2003. Extracting policy positions from political texts using words as data. American Political Science Review 97(2): 311–331.CrossRefGoogle Scholar
  6. 6.
    Lowe, W. 2008. Understanding wordscores. Political Analysis 16(4): 356–371.CrossRefGoogle Scholar
  7. 7.
    Lowe, Will. 2006. Yoshikoder: An open source multilingual content analysis tool for social scientists. The American Political Science Association Meeting, Philadelphia, USA. http://www.yoshikoder.org/courses/apsa2006/apsa-yk.pdf (accessed June. 26, 2010).
  8. 8.
    Mahoney, C. 2007. Lobbying success in the United States and the European Union. Journal of Public Policy 27(1): 35–56.CrossRefGoogle Scholar
  9. 9.
    Martin, L.W., and G. Vanberg. 2008. A robust transformation procedure for interpreting political text. Political Analysis 16(1): 93–100.CrossRefGoogle Scholar
  10. 10.
    Peng, Fuchun, Fangfang Feng, and Andrew McCallum. 2004. Chinese segmentation and new word detection using conditional random fields. Proceedings of the 20th International Conference on Computational Linguistics. Switzerland: Geneva. No 562.Google Scholar
  11. 11.
    Schneider, G., and K. Baltz. 2003. The power of specialization: How interest groups influence EU legislation. Revista di Politica Economica 93(1/2): 253–287.Google Scholar
  12. 12.
    Sullivan, J., and W. Lowe. 2010. Chen Shui-bian: On independence. The China Quarterly 203: 619–638.CrossRefGoogle Scholar
  13. 13.
    Sullivan, J., and B. Renz. 2010. Chinese migration: Still the major focus of Russian Far East/Chinese North East relations? The Pacific Review 23(2): 261–285.CrossRefGoogle Scholar

Copyright information

© Journal of Chinese Political Science/Association of Chinese Political Studies 2011

Authors and Affiliations

  1. 1.Institute of Political ScienceAcademia SinicaTaipei CityTaiwan

Personalised recommendations