Analyzing and annotating for sentiment analysis the socio-political debate on #labuonascuola

English. The paper describes a research about the socio-political debate on the reform of the education sector in Italy. It includes the development of an Italian dataset for sentiment analysis from two different comparable sources: Twitter and the online institutional platform implemented for supporting the debate. We describe the collection methodology, which is based on theoretical hypotheses about the communicative behavior of actors in the debate, the annotation scheme and the results of its application to the collected dataset. Finally, a comparative analysis of data is presented. Italiano. L’articolo descrive un progetto di ricerca sul dibattito socio-politico sulla riforma della scuola in Italia, che include lo sviluppo di un dataset per la sentiment analysis della lingua italiana estratto da due differenti fonti tra loro confrontabili: Twitter e la piattaforma istituzionale online implementata per supportare il dibattito. Viene evidenziata la metodologia utilizzata per la raccolta dei dati, basata su ipotesi teoriche circa le modalita di comunicazione in atto nel dibattito. Si descrive lo schema di annotazione, la sua applicazione ai dati raccolti, per concludere con un’analisi comparativa.

[1]  Bing Liu,et al.  Sentiment Analysis and Subjectivity , 2010, Handbook of Natural Language Processing.

[2]  Abelardo San Martín Núñez.  Ciberpragmática. El uso del lenguaje en Internet , 2001 .

[3]  Cristina Bosco,et al.  Developing Corpora for Sentiment Analysis: The Case of Irony and Senti-TUT , 2013, IEEE Intelligent Systems.

[4]  Diana Maynard,et al.  Automatic Detection of Political Opinions in Tweets , 2011, #MSM.

[5]  Björn W. Schuller,et al.  New Avenues in Opinion Mining and Sentiment Analysis , 2013, IEEE Intelligent Systems.

[6]  Cristina Bosco,et al.  Debate on political reforms in Twitter: A hashtag-driven analysis of political polarization , 2015, 2015 IEEE International Conference on Data Science and Advanced Analytics (DSAA).

[7]  Shrikanth S. Narayanan,et al.  A System for Real-time Twitter Sentiment Analysis of 2012 U.S. Presidential Election Cycle , 2012, ACL.

[8]  Diana Maynard,et al.  Who cares about Sarcastic Tweets? Investigating the Impact of Sarcasm on Sentiment Analysis. , 2014, LREC.

[9]  Paolo Rosso,et al.  A multidimensional approach for detecting irony in Twitter , 2013, Lang. Resour. Evaluation.

[10]  D. Sperber,et al.  Relevance: Communication and Cognition , 1989 .

[11]  Bing Liu,et al.  Sentiment Analysis and Opinion Mining , 2012, Synthesis Lectures on Human Language Technologies.

[12]  Tommaso Caselli,et al.  State of the Art Language Technologies for Italian: The EVALITA 2014 Perspective , 2015, Intelligenza Artificiale.

[13]  Johan Bos,et al.  Predicting the 2011 Dutch Senate Election Results with Twitter , 2012 .

[14]  Michelangelo Conoscenti.  The Reframer: An Analysis of Barack Obama’s Political Discourse (2004-2010) , 2011 .

[15]  C. Bazzanella.  Contextual constraints in CMC narratives , 2010 .

[16]  Stefano M. Iacus,et al.  Social media e sentiment analysis : l'evoluzione dei fenomeni sociali attraverso la rete , 2014 .

[17]  Paolo Rosso,et al.  SemEval-2015 Task 11: Sentiment Analysis of Figurative Language in Twitter , 2015, *SEMEVAL.

[18]  Theresa Wilson.  Fine-grained subjectivity and sentiment analysis: recognizing the intensity, polarity, and attitudes of private states , 2008 .

[19]  Paolo Rosso,et al.  On the difficulty of automatically detecting irony: beyond a simple case of negation , 2014, Knowledge and Information Systems.

[20]  Malvina Nissim,et al.  Sentiment analysis on Italian tweets , 2013, WASSA@NAACL-HLT.

[21]  Isabell M. Welpe,et al.  Predicting Elections with Twitter: What 140 Characters Reveal about Political Sentiment , 2010, ICWSM.

[22]  Malvina Nissim,et al.  Overview of the Evalita 2014 SENTIment POLarity Classification Task , 2014 .

[23]  Robert M. Entman,et al.  Framing: Toward Clarification of a Fractured Paradigm , 1993 .