Thematic content analysis using supervised machine learning: An empirical evaluation using German online news

Quality and Quantity

Volumn 47, Issue 2, 2013, Pages 761-773

  Scharkow, Michael ^a  

^a UNIVERSITY OF HOHENHEIM   (Germany)

Author keywords

Bayesian classifier; Content analysis; Machine learning; Online news

Indexed keywords

EID: 84872663386     PISSN: 00335177     EISSN: 15737845     Source Type: Journal    
DOI: 10.1007/s11135-011-9545-7     Document Type: Article

References (48)

1. Alexa M., Zuell C.: Text analysis software: Commonalities, differences and limitations: The results of a review. Qual. Quant. 34, 299-321 (2000).
2. Assis, F.: OSBF-Lua-A text classification module for Lua-The importance of the training method. In: Fifteenth TREC, Citeseer, Gaithersburg (2006).
3. Braschler M., Ripplinger B.: How effective is stemming and decompounding for German text retrieval?. Inf. Retrieval 7, 291-316 (2004).
4. Bruns, T., Marcinkowski, F.: Politische information im Fernsehen [Political information in television]. Leske + Budrich, Opladen (1997).
5. Cormack G. V., Lynam T. R.: Online supervised spam filter evaluation. ACM Trans. Inf. Sys. 25(3), 11 (2007).
6. Doerfel M., Barnett G.: The use of Catpac for text analysis. Cult. Anthropol. Methods J. 8(2), 4-7 (1996).
7. Durant K, Smith, M.: Predicting the political sentiment of Web Log posts using supervised machine learning techniques coupled with feature selection. In: Advances in Web Mining and Web Usage Analysis: 8th International Workshop on Knowledge Discovery on the Web, Webkdd 2006, pp. 187-206. Springer-Verlag, New York (2007).
8. Eilders C.: News factors and news decisions. Theoretical and methodological advances in Germany. Communications 31(1), 5-24 (2006).
9. Eugenio B. D., Glass M.: The kappa statistic: A second look. Comput. Ling. 30, 95-101 (2004).
10. Evans M., McIntosh W., Lin J., Cates C.: Recounting the courts? Applying automated content analysis to enhance empirical legal research. J. Emp. Legal Stud. 4(4), 1007-1039 (2007).
11. Finn, A., Kushmerick, N., Smyth, B.: Fact or fiction: Content classification for digital libraries. In: DELOS Workshop: Personalisation and Recommender Systems in Digital Libraries, Dublin (2001).
12. Fretwurst, B.: Nachrichten im Interesse der Zuschauer. Eine konzeptionelle und empirische Neubestimmung der Nachrichtenwerttheorie [News in the viewer's interest. A conceptual and empirical re-evaluation of news values theory]. UVK Verlag, Konstanz (2008).
13. GÖFAK Medienforschung: Fernsehanalyse zum Bundestagswahlkampf 2009. Methodenbericht GLES1401 der German Longitudinal Election Study [content analysis of tv coverage for the bundestag election 2009]. http://www. gesis. org/fileadmin/upload/dienstleistung/forschungsdatenzentren/gles/SecureDownload/frageboegen/GLES1401_Pre1. 0%20-%20Methodenbericht. pdf (2010).
14. Hayes A. F., Krippendorff K.: Answering the call for a standard reliability measure for coding data. Commun. Methods Meas. 1(1), 77-89 (2007).
15. Hillard, D., Purpura, S., Wilkerson, J.: An active learning framework for classifiying political text. In: Annual Meeting of the Midwest Political Science Association, Chicago (2007).
16. Hillard D., Purpura S., Wilkerson J.: Computer-assisted topic classification for mixed-methods social science research. J. Inf. Technol. Polit. 4(4), 31-46 (2008).
17. Holsti O.: Content Analysis for the Social Sciences and Humanities. Addison-Wesley, Reading (1969).
18. Hotho A., Nürnberger A., Paaß G.: A brief survey of text mining. LDV Forum GLDV J. Comput. Ling. Lang. Technol. 20(1), 19-62 (2005).
19. Iker H., Harway N.: A computer systems approach toward the recognition and analysis of content. In: Gerbner, G. (ed.) The Analysis of Communication Content. Developments in Scientific Theories and Computer Techniques., pp. 381-405. Wiley, New York (1969).
20. King G., Lowe W.: An Automated Information Extraction Tool for International Conflict Data with Performance as Good as Human Coders: A Rare Events Evaluation Design. Int. Organ. 57(3), 617-642 (2003).
21. Krippendorff K.: Content Analysis: An Introduction to Its Methodology, 2nd edn. Sage, London (2004a).
22. Krippendorff K.: Reliability in content analysis. Human Commun. Res. 30, 411-433 (2004b).
23. Landmann J., Züll C.: Identifying events using computer-assisted text analysis. Soc. Sci. Comput. Rev. 26(4), 483-497 (2008).
24. Lasswell H., Namenwirth J.: The Lasswell Value Dictionary. Yale University Press, New Haven (1968).
25. Laver M., Benoit K., Garry J.: Extracting policy positions from political texts using words as data. Am. Polit. Sci. Rev. 97, 311-331 (2003).
26. Leopold E., Kindermann J.: Text categorization with support vector machines. How to represent texts in input space?. Mach. Learn. 46(1-3), 423-444 (2002).
27. Li, J., Ezeife, C.: Cleaning web pages for effective web content mining. In: Database and Expert Systems Applications. Springer, Berlin, pp. 560-571 (2006).
28. Manning C. D., Schütze H.: Foundations of Statistical Natural Language Processing. MIT Press, Cambridge, MA (1999).
29. Monroe B. L., Schrodt P. A.: Introduction to the special issue: The statistical analysis of political text. Polit. Anal. 16(4), 351-355 (2008).
30. Pang B., Lee L.: Opinion mining and sentiment analysis. Foundations Trends Inf. Retrieval 2(1-2), 1-135 (2008).
31. Pennings P., Keman H.: Towards a new methodology of estimating party policy positions. Qual. Quant. 36(1), 55-79 (2002).
32. Popping, R.: Computer-assisted Text Analysis. Sage, Thousand Oaks, CA (2000).
33. Porter M.: An algorithm for suffix stripping. Program 14(3), 130-137 (1980).
34. Potter W. J., Levine-Donnerstein D.: Rethinking validity and reliability in content analysis. J. Appl. Commun. Res. h 27(3), 258-284 (1999).
35. Purpura, S., Hillard, D.: Automated classification of congressional legislation. In: Proceedings of the 2006 International Conference on Digital Government Research, pp. 219-225 (2006).
36. Reidsma D., Carletta J.: Reliability measurement without limits. Comput. Ling. 34(3), 319-326 (2008).
37. Roberts, C.: Introduction. In: Roberts C (ed) Text Analysis for the Social Sciences: Methods for Drawing Statistical Inferences from Texts and Transcripts, pp. 1-8. Lawrence Erlbaum Associates, Mahwah, NJ (1997).
38. Roberts C. W.: A conceptual framework for quantitative text analysis. Qual. Quant. 34(3), 259-274 (2000).
39. Schrodt, P.: Automated Production of High-Volume, Near-Real-Time Political Event Data. Paper presented at the 2010 APSA Conference (2010).
40. Schrodt P., Davis S., Weddle J.: Political science: KEDS-a program for the machine coding of event data. Soc. Sci. Comput. Rev. 12(4), 561 (1994).
41. Sebastiani F.: Machine learning in automated text categorization. ACM Comput. Surv. 34(1), 1-47 (2002).
42. Sheng, V., Provost, F., Ipeirotis, P.: Get another label? Improving data quality and data mining using multiple, noisy labelers. In: Proceedings of the 14th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 614-622. ACM, Las Vegas, NV (2008).
43. Siefkes, C., Assis, F., Chhabra, S., Yerazunis, W. S.: Combining winnow and orthogonal sparse bigrams for incremental spam filtering. In: Proceedings of the 8th European Conference on Principles and Practice of Knowledge Discovery in Databases, Pisa, pp. 410-421 (2004).
44. Stone P., Dunphy D., Smith M., Ogilvie D.: The General Inquirer: A Computer Approach to Content Analysis. The MIT Press, Cambridge, MA (1966).
45. van Atteveldt, W.: Semantic Network Analysis: Techniques for Extracting, Representing, and Querying Media Content. BookSurge Publishers, Charleston, SC (2008).
46. van Atteveldt, W., Kleinnijenhuis, J., Ruigrok, N.: Semantic network analysis: A two-step approach for flexible, reusable, and combinable content analysis. Paper presented at the 2010 ICA conference, Singapore (2010).
47. van Cuilenburg J. J., Kleinnijenhuis J., de Ridder J. A.: Artificial intelligence and content analysis. Qual. Quant. 22(1), 65-97 (1988).
48. Weare C., Lin W.: Content analysis of the World Wide Web: Opportunities and challenges. Soc. Sci. Comput. Rev. 18(3), 272-292 (2000).