The New Release of CORPS: A Corpus of Political Speeches Annotated with Audience Reactions

  • Marco Guerini
  • Danilo Giampiccolo
  • Giovanni Moretti
  • Rachele Sprugnoli
  • Carlo Strapparava
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7688)


In this paper we present the new release of CORPS (CORpus of tagged Political Speeches) that contains transcripts of political speeches tagged with audience reactions, such as APPLAUSE or LAUGHTER. The corpus has been built with the goal of allowing automatic processing of the stored data. These tags signal hot-spots about persuasive communication and can be usefully employed in many theoretical and applied fields, providing insights well beyond those of traditional word-count approaches. After introducing the main characteristics of the corpus and some quantitative descriptions, we discuss possible uses of this resource.


persuasion political communication annotated corpora public speaking natural language processing 


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  1. 1.
    Atkinson, J.: Public speaking and audience response: some techniques for inviting applause. In: Structures of Social Action, pp. 370–409. Cambridge University Press, Cambridge (1984)Google Scholar
  2. 2.
    Benoit, K., Laver, M.: Estimating Irish party positions using computer wordscoring: The 2002 elections. Irish Political Studies 17(2) (2003)Google Scholar
  3. 3.
    Bertoldi, N., Brugnara, F., Cettolo, M., Federico, M., Giuliani, D.: Cross-task portability of a broadcast news speech recognition system. Speech Communication 38(3-4), 335–347 (2002)CrossRefzbMATHGoogle Scholar
  4. 4.
    Bevitori, C.: Engendering conflict? A corpus-assisted analysis of women MPs positioning on the war in Iraq. Textus 20(1), 137–158 (2007)Google Scholar
  5. 5.
    Bligh, M.C., Kohles, J.C., Meindl, J.R.: Charisma under crisis: Presidential leadership, rhetoric, and media responses before and after the September 11th terrorist attacks. The Leadership Quarterly 15(2), 211–239 (2004)CrossRefGoogle Scholar
  6. 6.
    Bull, P., Noordhuizen, M.: The mistiming of applause in political speeches. Journal of Language and Social Psychology 19, 275–294 (2000)CrossRefGoogle Scholar
  7. 7.
    Conoscenti, M.: The Reframer: An Analysis of Barack Obama’s Political Discourse (2004-2010). Bulzoni, Roma (2011)Google Scholar
  8. 8.
    Cousins, K., Mcintosh, W.: More than typewriters, more than adding machines: Integrating information technology into political research. Quality and Quantity 39, 581–614 (2005)CrossRefGoogle Scholar
  9. 9.
    Dyson, S.B.: Text Annotation and the Cognitive Architecture of Political Leaders: British Prime Ministers from 1945-2008. Journal of Information Technology & Politics 5(1), 7–18 (2008)CrossRefGoogle Scholar
  10. 10.
    Franzosi, R.: From Words to Numbers: Narrative, Data, and Social Science. Cambridge University Press, Cambridge (2004)Google Scholar
  11. 11.
    Guerini, M., Strapparava, C., Stock, O.: Corps: A corpus of tagged political speeches for persuasive communication processing. Journal of Information Technology & Politics 5(1), 19–32 (2008)CrossRefGoogle Scholar
  12. 12.
    Guerini, M., Strapparava, C., Stock, O.: Valentino: A tool for valence shifting of natural language texts. In: Proceedings of LREC 2008, Marrakech, Morocco (2008)Google Scholar
  13. 13.
    Heritage, J., Greatbatch, D.: Generating applause: a study of rhetoric and response at party political conferences. American Journal of Sociology 92, 110–157 (1986)CrossRefGoogle Scholar
  14. 14.
    Hermann, M.G.: Assessing leadership style: trait analysis. In: The Psychological Assessment of Political Leaders, pp. 178–214. Lawrence Erlbaum Publishing Co. (2003)Google Scholar
  15. 15.
    Hu, Q., Goodman, F., Boykin, S., Fish, R., Greiff, W., Jones, S., Moore, S.: Automatic detection, indexing, and retrieval of multiple attributes from cross-lingual multimedia data (2008)Google Scholar
  16. 16.
    Jing, H.: Usage of wordnet in natural language generation. In: Harabagiu, S. (ed.) Proceedings of the Conference on the Use of WordNet in Natural Language Processing Systems, pp. 128–134. Association for Computational Linguistics, Somerset (1998)Google Scholar
  17. 17.
    Klebanov, B.B., Diermeier, D., Beigman, E.: Automatic Annotation of Semantic Fields for Political Science Research. Journal of Information Technology & Politics 5(1), 95–120 (2008)CrossRefGoogle Scholar
  18. 18.
    Laver, M., Benoit, K.: Locating tds in policy spaces: Wordscoring Dail speeches. Irish Political Studies 17(1) (2002)Google Scholar
  19. 19.
    Laver, M., Benoit, K., Garry, J.: Extracting policy positions from political texts using words as data. American Political Science Review 97(2), 311–331 (2003)CrossRefGoogle Scholar
  20. 20.
    Laver, M., Garry, J.: Estimating policy positions from political texts. American Journal of Political Science 44(3), 619–634 (2000)CrossRefGoogle Scholar
  21. 21.
    Poggi, I., Vincze, L.: Gesture, gaze and persuasive strategies in political discourse. In: Kipp, M., Martin, J.-C., Paggio, P., Heylen, D. (eds.) Multimodal Corpora. LNCS, vol. 5509, pp. 73–92. Springer, Heidelberg (2009)CrossRefGoogle Scholar
  22. 22.
    Purpura, S., Hillard, D.: Automated classification of congressional legislation. In: Proceedings of the Seventh International Conference on Digital Government Research, San Diego, CA (2006)Google Scholar
  23. 23.
    Purpura, S., Hillard, D., Howard, P.: A comparative study of human coding and context analysis against support vector machines (svm) to differentiate campaign emails by party and issues (2006)Google Scholar
  24. 24.
    Strapparava, C., Guerini, M., Stock, O.: Predicting persuasiveness in political discourses. In: Proceedings of the Seventh conference on International Language Resources and Evaluation, LREC 2010 (2010)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Marco Guerini
    • 1
  • Danilo Giampiccolo
    • 2
  • Giovanni Moretti
    • 2
  • Rachele Sprugnoli
    • 2
  • Carlo Strapparava
    • 3
  1. 1.Trento-RisePovoItaly
  2. 2.CELCTPovoItaly
  3. 3.irstFBKPovoItaly

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