Analyzing and annotating for sentiment analysis the socio-political debate on #labuonascuola
Authors: Marco Stranisci, Cristina Bosco, Viviana Patti, Delia Irazù Hernàndez Farìas (Università di Torino, Cooperativa weLaika, University of Valencia, 2015)
Topic: Sentiment, Emotion, Irony, Hate

Abstract
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.

Introduction
The widespread diffusion of social media in the last years led to a significant growth of interest in the field of opinion and sentiment analysis of user generated contents AUTHOR. The first applications of these techniques were focusing on the users' reviews for commercial products and services (e.g. books, shoes, hotels and restaurants), but they quickly extended their scope to other interesting topics, like politics. Applications of sentiment analysis to politics can be mainly investigated under two perspectives: on one hand, many works focus on the possibility of predicting the election results through the analysis of the sentiment conveyed by data extracted from social media AUTHOR; on the other hand, the power of social media as ``a trigger that can lead to administrative, political and societal changes" AUTHOR is also an interesting subject to investigate AUTHOR. This paper mainly focuses on the last perspective. Our aim is indeed the creation of a manual annotated corpus for sentiment analysis to investigate the dynamics of communication between politics and civil society as structured in Twitter and social media. We focused from the beginning our attention mainly on Twitter because of the relevance explicitly given to this media in the communication dynamics of the current government. In order to describe and model this communicative behavior of the government, we assume the theoretical framework known in literature as framing, which consists in making especially salient in communication some selected aspect of a perceived reality AUTHOR. The data selected to create the corpus have been chosen by analyzing in Twitter and other contexts the diffusion of three hashtags, i.e. #labuonascuola, #italicum, #jobsact. In particular, we focus on #labuonascuola (the good school), which was coined to communicate the school reform proposed by the actual government. A side effect of our work is the development of a new lexical resource for sentiment analysis in Italian, a currently under-resourced language. Among the existing resources let us mention Senti-TUT AUTHOR, which has been exploited together with the TWITA corpus AUTHOR for building the training and testing datasets in the SENTIment POLarity Classification shared task AUTHOR recently proposed during the last edition of the evaluation campaign for Italian NLP tools and resources AUTHOR. The Sentipolc's dataset includes tweets collected during the alternation between Berlusconi and Monti on the chair of Prime Minister of the Italian government. The current proposal aims at expanding the available Italian Twitter data annotated with sentiment labels on the topic of politics, and it is compatible with the existing datasets w.r.t. the annotation scheme and other features. The paper is organized as follows. The next section describes the dataset mainly focusing on collection. In the section 3 we describe the annotation applied to the collected data and the annotation process. Section 4 concludes the paper with a discussion of the analysis applied to the dataset.

Conclusion
The paper describes a project for the analysis of a socio-political debate in a sentiment analysis perspective. A novel resource is presented by describing the collection and the annotation of the dataset organized in two subcorpora according to the source the texts have been extracted from: one from Twitter and one from the institutional online consultation platform. A first analysis of the resulting dataset is presented, which takes into account also a comparative perspective.