Predicting Persuasiveness in Political Discourses

Carlo Strapparava, Marco Guerini, Oliviero Stock


Abstract
In political speeches, the audience tends to react or resonate to signals of persuasive communication, including an expected theme, a name or an expression. Automatically predicting the impact of such discourses is a challenging task. In fact nowadays, with the huge amount of textual material that flows on the Web (news, discourses, blogs, etc.), it can be useful to have a measure for testing the persuasiveness of what we retrieve or possibly of what we want to publish on Web. In this paper we exploit a corpus of political discourses collected from various Web sources, tagged with audience reactions, such as applause, as indicators of persuasive expressions. In particular, we use this data set in a machine learning framework to explore the possibility of classifying the transcript of political discourses, according to their persuasive power, predicting the sentences that possibly trigger applause. We also explore differences between Democratic and Republican speeches, experiment the resulting classifiers in grading some of the discourses in the Obama-McCain presidential campaign available on the Web.
Anthology ID:
L10-1414
Volume:
Proceedings of the Seventh International Conference on Language Resources and Evaluation (LREC'10)
Month:
May
Year:
2010
Address:
Valletta, Malta
Editors:
Nicoletta Calzolari, Khalid Choukri, Bente Maegaard, Joseph Mariani, Jan Odijk, Stelios Piperidis, Mike Rosner, Daniel Tapias
Venue:
LREC
SIG:
Publisher:
European Language Resources Association (ELRA)
Note:
Pages:
Language:
URL:
http://www.lrec-conf.org/proceedings/lrec2010/pdf/607_Paper.pdf
DOI:
Bibkey:
Cite (ACL):
Carlo Strapparava, Marco Guerini, and Oliviero Stock. 2010. Predicting Persuasiveness in Political Discourses. In Proceedings of the Seventh International Conference on Language Resources and Evaluation (LREC'10), Valletta, Malta. European Language Resources Association (ELRA).
Cite (Informal):
Predicting Persuasiveness in Political Discourses (Strapparava et al., LREC 2010)
Copy Citation:
PDF:
http://www.lrec-conf.org/proceedings/lrec2010/pdf/607_Paper.pdf