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.

Predicting Persuasiveness in Political Discourses / Strapparava, C.; Guerini, M.; Stock, O.. - (2010), pp. 1342-1345. ((Intervento presentato al convegno Seventh conference on International Language Resources and Evaluation (LREC'10) tenutosi a Valletta, Malta nel 19-21 May 2010.

Predicting Persuasiveness in Political Discourses

C. Strapparava;M. Guerini;
2010

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.
Proceedings of the 7th International Conference on Language Resources and Evaluation (LREC 2010)
-
France
European Language Resources Association (ELRA)
Strapparava, C.; Guerini, M.; Stock, O.
Predicting Persuasiveness in Political Discourses / Strapparava, C.; Guerini, M.; Stock, O.. - (2010), pp. 1342-1345. ((Intervento presentato al convegno Seventh conference on International Language Resources and Evaluation (LREC'10) tenutosi a Valletta, Malta nel 19-21 May 2010.
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11572/343415
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