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Lexical Cohesion Analysis of Political Speech

Published online by Cambridge University Press:  04 January 2017

Beata Beigman Klebanov
Affiliation:
Kellogg School of Management, MEDS Department, Leverone Hall 554, 2001 Sheridan Road, Evanston, IL60208-2009
Daniel Diermeier
Affiliation:
Kellogg School of Management, MEDS Department, Leverone Hall 554, 2001 Sheridan Road, Evanston, IL60208-2009
Eyal Beigman
Affiliation:
Kellogg School of Management, MEDS Department, Leverone Hall 554, 2001 Sheridan Road, Evanston, IL60208-2009

Abstract

This article presents a novel automatic method of text analysis aimed at discovering patterns of lexical cohesion in political speech. The unit of analysis are groups of words with related meanings; the software is based on the results of a multiperson annotation experiment that captures reliably identified connections between words in a text. We illustrate the advantages of such a representation by juxtaposing results of a detailed hand-made analysis of Margaret Thatcher's rhetoric with analysis based on the automatically detected groups of words. We both corroborate previous findings regarding Thatcher's rhetorical tools and illuminate additional elements thereof. We suggest that lexical cohesion analysis is a promising technique to bridge the gap between quantitative and qualitative analyses of text as political material, by establishing units that are both robust enough to enable comprehensive coverage and coherent enough to support direct interpretation.

Type
Special Issue: The Statistical Analysis of Political Text
Copyright
Copyright © The Author 2008. Published by Oxford University Press on behalf of the Society for Political Methodology 

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Footnotes

Author's note: Beata Beigman Klebanov is a post-doctoral fellow at the Ford Center for Global Citizenship at the Kellogg School of Management, and at Northwestern Institute for Complex Systems, Northwestern University, Evanston, IL. Her research focuses on semantic analysis of texts from theoretical, experimental, computational, and applied perspectives. Daniel Diermeier is the IBM Distinguished Professor of Regulation and Competitive Practice at the Department of Managerial Economics and Decision Sciences (MEDS), Director of the Ford Motor Company Center for Global Citizenship, all at the Kellogg School of Management; and Professor of Political Science and a Research Fellow at the Northwestern Institute on Complex Systems (NICO), all at Northwestern University. Professor Diermeier specializes in formal models of politics with an emphasis on legislative institutions. Recently he has done extensive work in the use of computational linguistics to study ideology and belief formation in political speech. In 2007 he received the Aspen Institute's Faculty Pioneer Award. Eyal Beigman is a post-doctoral fellow at the Center for Mathematical Studies in Economics and Management Science at the Kellogg School of Management, and at McCormick School of Engineering, Northwestern University, Evanston, IL. His research interests include structural and strategic analysis of voting systems, political speech, and economics of identity. He is also working on a project studying mechanisms and policies in the wireless telecommunication industry.

Conflict of interest statement: None declared.

References

Azar, Y., Fiat, A., Karlin, A. R., McSherry, F., and Saia, J. 2001. Spectral analysis of data. In ACM Symposium on Theory of Computing, eds. Vitter, J. S., Spirakis, P., and Yannakakis, M., 619626. New York: Association for Computing Machinery.Google Scholar
Barzilay, R., and Elhadad, M. 1997. Using lexical chains for text summarization. In Proceedings of the Association for Computational Linguistics (ACL) Intelligent Scalable Text Summarization Workshop, eds. Mani, I. and Maybury, M., 8690. Somerset, NJ: Association for Computational Linguistics.Google Scholar
Beigman Klebanov, B. 2006. Measuring semantic relatedness using people and WordNet. In Proceedings of the North American Chapter of the Association for Computational Linguistics, Companion Volume: Short Papers, eds. Moore, R. C., Bilmes, J. A., Chu-Carroll, J., and Sanderson, M., 1317. New York: The Association for Computational Linguistics.Google Scholar
Beigman Klebanov, B. 2007. Experimental and computational investigation of lexical cohesion in English texts. PhD thesis, The Hebrew University of Jerusalem, Jerusalem, Israel.Google Scholar
Beigman Klebanov, B., Diermeier, D., and Beigman, E. 2008. Automatic annotation of semantic fields for political science research. Journal of Information Technology and Politics 5(1): 95120.CrossRefGoogle Scholar
Beigman Klebanov, B., and Shamir, E. 2006. Reader-based exploration of lexical cohesion. Language Resources and Evaluation 40(2): 109–26.Google Scholar
Callaghan, K., and Schnell, F. 2001. Assessing the democratic debate: How the news media frame elite policy discourse. Political Communication 18(2): 183213.Google Scholar
Caruana, R., and Niculescu-Mizil, A. 2006. An empirical comparison of supervised learning algorithms. In Proceedings of International Conference on Machine Learning, eds. Cohen, W. and Moore, A., 161168. New York: Association for Computing Machinery.Google Scholar
Charteris-Black, J. 2004. Corpus approaches to critical metaphor analysis. Houndmills, UK and New York: Palgrave MacMillan.CrossRefGoogle Scholar
Charteris-Black, J. 2005. Politicians and rhetoric: The persuasive power of metaphors. Houndmills, UK and New York: Palgrave MacMillan.CrossRefGoogle Scholar
Chong, D., and Druckman, J. N. 2008. Framing public opinion in competitive democracies. American Political Science Review 101: 637–56.Google Scholar
Converse, P. E. 1964. The nature of belief systems in mass publics. In Ideology and discontent, ed. Apter, D. E., 206261. London: Free Press of Glencoe.Google Scholar
Deerwester, S., Dumais, S. T., Furnas, G. W., Landauer, T. K., and Harshman, R. 1990. Indexing by latent semantic analysis. Journal of the American Society for Information Science 41: 391407.3.0.CO;2-9>CrossRefGoogle Scholar
Diermeier, D., Kaufmann, S., Yu, B., and Godbout, J.-F. 2007. Language and ideology in Congress. Paper presented at the Annual Meeting of the Midwest Political Science Association, Chicago, April 12–15, 2007.Google Scholar
Druckman, J. N., and Nelson, K. R. 2003. Framing and deliberation: How citizens’ conversations limit elite influence. American Journal of Political Science 47: 729745.CrossRefGoogle Scholar
Entman, R. M. 2003. Cascading activation: Contesting the White House's frame after 9/11. Political Communication 20: 415432.CrossRefGoogle Scholar
Fairclough, N. 2003. Language and power. 2nd ed. New York: Pearson Education Ltd.Google Scholar
Gurevych, I., and Strube, M. 2004. Semantic similarity applied to spoken dialogue summarization. In Proceedings of International Conference on Computational Linguistics (COLING), pp. 764770. Geneva, Switzerland, August 23–27, 2004. Retrieved Oct 8, 2008 from: http://www.aclweb.org/anthology-new/C/C04/C04-1110.pdf.Google Scholar
Halliday, M., and Hasan, R. 1976. Cohesion in English. London: Longman Group Ltd.Google Scholar
Hasan, R. 1984. Coherence and cohesive harmony. In Understanding reading comprehension, ed. Flood, J., 181219. Newark, DE: International Reading Association.Google Scholar
Hirst, G., and Budanitsky, A. 2005. Correcting real-word spelling errors by restoring lexical cohesion. Natural Language Engineering 11(1): 87111.CrossRefGoogle Scholar
Inkpen, D., and Desilets, A. 2005. Semantic similarity for detecting recognition errors in automatic speech transcripts. In Proceedings of Empirical Methods in Natural Language Processing Conference. Vancouver, Canada, October 6–8, 2005. Retrieved Oct 8, 2008 from: http://www.aclweb.org/anthology-new/H/H05/H05-1007.pdf.Google Scholar
Johnson, M. 1987. The body in the mind. Chicago, IL: Chicago University Press.Google Scholar
Kiss, G., Armstrong, C., Milroy, R., and Piper, J. 1973. An associative thesaurus of English and its computer analysis. In The computer and literary studies, eds. Aitken, A., Bailey, R., and Hamilton-Smith, N. Edinburgh: University Press.Google Scholar
Kontostathis, A., and Pottenger, W. M. 2006. A framework for understanding LSI performance. Information Processing and Management 42: 5673.CrossRefGoogle Scholar
Kuĉera, H., and Francis, W. N. 1967. Computational analysis of present-day American English. Providence, RI: Brown University Press.Google Scholar
Lakoff, G. 1991. Metaphor and war: The metaphor system used to justify war in the Gulf. Peace Research 23: 2532.Google Scholar
Lakoff, G. 2002. Moral politics: How liberals and conservatives think. 2nd ed. Chicago, IL: The University of Chicago Press.CrossRefGoogle Scholar
Lakoff, G., and Johnson, M. 1980. Metaphors we live by. Chicago, IL: The Chicago University Press.Google Scholar
Laver, M., and Benoit, K. 2002. Locating TDs in policy spaces: Wordscoring Dáil speeches. Irish Political Studies 17(1): 5973.CrossRefGoogle Scholar
Laver, M., Benoit, K., and Garry, J. 2003. Extracting policy positions from political texts using words as data. American Political Science Review 97(2): 311–31.CrossRefGoogle Scholar
Levin, I. P., Schneider, S. L., and Gaeth, G. J. 1998. All frames are not created equal. Organizational Behavior and Human Decision Processes 76: 149–88.CrossRefGoogle Scholar
Levy, J. 2003. Applications of prospect theory to political science. Synthese 135: 215–41.CrossRefGoogle Scholar
Mautner, G. 2001. British national identity in the European context. In Attitudes towards Europe: Language in the unification process, eds. Musolff, A., Good, C., Points, P., and Wittlinger, R., 322. Hants, UK: Ashgate Publishing Ltd.Google Scholar
Miller, G. 1990. WordNet: An on-line lexical database. International Journal of Lexicography 3(4): 235312.CrossRefGoogle Scholar
Moldovan, D., and Novischi, A. 2002. Lexical chains for question answering. In Proceedings of International Conference on Computational Linguistics, Taipei, Taiwan, Aug 26–30, 2002. Retrieved Oct 8, 2008 from: http://www.aclweb.org/anthology-new/C/C02/C02-1167.pdf.Google Scholar
Monroe, B. L., and Maeda, K. 2004. Talk's cheap: Text-based estimation of rhetorical ideal points. Paper presented at the annual meeting of the Society for Political Methodology. Stanford University, July 29–31, 2004.Google Scholar
Morris, J., and Hirst, G. 1991. Lexical cohesion, the thesaurus, and the structure of text. Computational linguistics 17(1): 2148.Google Scholar
Nelson, D. L., McEvoy, C. L., and Schreiber, T. A. 2004. The University of South Florida word association, rhyme, and word fragment norms. Behavior Research Methods, Instruments, & Computers 36(3): 402–7.CrossRefGoogle ScholarPubMed
Poole, K. T. 2003. Changing minds? Not in Congress. Unpublished Manuscript.Google Scholar
Poole, K. T., and Rosenthal, H. 1997. Congress: a political-economic history of roll call voting. New York: Oxford.Google Scholar
Poole, K. T., and Rosenthal, H. 2007. Ideology and Congress. Piscataway, NJ: Transaction Publisher.Google Scholar
Quinlan, J. R. 1993. C4.5: Programs for machine learning. Morgan Kaufmann Publishers.Google Scholar
Quinn, K. M., Monroe, B. L., Colaresi, M., Crespin, M. H., and Radev, D. R. 2006. An automated method of topic-coding legislative speech over time with application to the 105th-108th U.S. Senate. Paper presented at the annual meeting of the Midwest Political Science Association. Chicago, April 20–23, 2006.Google Scholar
Ratnaparkhi, A. 1996. A maximum entropy model for part-of-speech tagging. In Proceedings of Empirical Methods in Natural Language Processing Conference 133–42. Philadelphia, PA, Ma 17–18, 1996. Retrieved Oct 8, 2008, from: http://www.aclweb.org/anthology-new/W/W96/W96-0213.pdf.Google Scholar
Rennie, J. 2000. WordNet::QueryData: a Perl module for accessing the WordNet database. http://people.csail.mit.edu/∼jrennie/WordNet, accessed Oct 8, 2008.Google Scholar
Silber, G., and McCoy, K. 2002. Efficiently computed lexical chains as an intermediate representation for automatic text summarization. Computational Linguistics 28(4): 487496.CrossRefGoogle Scholar
Simon, A. F., and Xenos, M. 2004. Dimensional reduction of word-frequency data as a substitute for intersubjective content analysis. Political Analysis 12: 6375.CrossRefGoogle Scholar
Slapin, J. B., and Proksch, S.-O. 2007. A scaling model for estimating time-series party positions from texts. Paper presented at the Annual Meeting of the Midwest Political Science Association, Chicago, April 12–15, 2007.Google Scholar
Stotsky, S. 1983. Types of lexical cohesion in expository writing: Implications for developing the vocabulary of academic discourse. College Composition and Communication 34(4): 430–46.CrossRefGoogle Scholar
Thomas, M., Pang, B., and Lee, L. 2006. Get out the vote: Determining support or opposition from Congressional floor-debate transcripts. In Proceedings of Empirical Methods in Natural Language Processing Conference, pp. 327–35.Google Scholar
Tversky, A., and Kahneman, D. 1981. The framing of decisions and the psychology of choice. Science 211: 453–8.CrossRefGoogle Scholar

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