Summary of the paper
| Title | Predicting Persuasiveness in Political Discourses |
|---|---|
| Authors | Carlo Strapparava, Marco Guerini and Oliviero Stock |
| Abstract | In political speeches, the audience tends to react or resonate tosignals of persuasive communication, including an expected theme, aname or an expression. Automatically predicting the impact of suchdiscourses 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 weretrieve or possibly of what we want to publish on Web. In this paper we exploit a corpus of political discourses collected fromvarious Web sources, tagged with audience reactions, such asapplause, as indicators of persuasive expressions. In particular, we use this data set in a machine learning framework to explorethepossibility of classifying the transcript of political discourses,according to their persuasive power, predicting the sentences thatpossibly trigger applause. We also explore differences betweenDemocratic and Republican speeches, experiment the resultingclassifiers in grading some of the discourses in theObama-McCain presidential campaign available on the Web. |
| Language | Document Classification, Text categorisation |
| Topics | Emotion Recognition/Generation, Discourse annotation, representation and processing, Document Classification, Text categorisation |
Full paper  |
Predicting Persuasiveness in Political Discourses |
| Bibtex | @InProceedings{STRAPPARAVA10.607, author = {Carlo Strapparava, Marco Guerini and Oliviero Stock}, title = {Predicting Persuasiveness in Political Discourses}, booktitle = {Proceedings of the Seventh conference on International Language Resources and Evaluation (LREC'10)}, year = {2010}, month = {may}, date = {19-21}, address = {Valletta, Malta}, editor = {Nicoletta Calzolari (Conference Chair), Khalid Choukri, Bente Maegaard, Joseph Mariani, Jan Odjik, Stelios Piperidis, Mike Rosner, Daniel Tapias}, publisher = {European Language Resources Association (ELRA)}, isbn = {2-9517408-6-7}, language = {english} } |