Summary of the paper

Title Annotating Sentiment and Irony in the Online Italian Political Debate on #labuonascuola
Authors Marco Stranisci, Cristina Bosco, Delia Irazú Hernández Farías and Viviana Patti
Abstract In this paper we present the TWitterBuonaScuola corpus (TW-BS), a novel Italian linguistic resource for Sentiment Analysis, developed with the main aim of analyzing the online debate on the controversial Italian political reform “Buona Scuola” (Good school), aimed at reorganizing the national educational and training systems. We describe the methodologies applied in the collection and annotation of data. The collection has been driven by the detection of the hashtags mainly used by the participants to the debate, while the annotation has been focused on sentiment polarity and irony, but also extended to mark the aspects of the reform that were mainly discussed in the debate. An in-depth study of the disagreement among annotators is included. We describe the collection and annotation stages, and the in-depth analysis of disagreement made with Crowdflower, a crowdsourcing annotation platform.
Topics Opinion Mining / Sentiment Analysis, Corpus (Creation, Annotation, etc.), Social Media Processing
Full paper Annotating Sentiment and Irony in the Online Italian Political Debate on #labuonascuola
Bibtex @InProceedings{STRANISCI16.1063,
  author = {Marco Stranisci and Cristina Bosco and Delia Irazú Hernández Farías and Viviana Patti},
  title = {Annotating Sentiment and Irony in the Online Italian Political Debate on #labuonascuola},
  booktitle = {Proceedings of the Tenth International Conference on Language Resources and Evaluation (LREC 2016)},
  year = {2016},
  month = {may},
  date = {23-28},
  location = {Portoro┼ż, Slovenia},
  editor = {Nicoletta Calzolari (Conference Chair) and Khalid Choukri and Thierry Declerck and Sara Goggi and Marko Grobelnik and Bente Maegaard and Joseph Mariani and Helene Mazo and Asuncion Moreno and Jan Odijk and Stelios Piperidis},
  publisher = {European Language Resources Association (ELRA)},
  address = {Paris, France},
  isbn = {978-2-9517408-9-1},
  language = {english}
 }
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