Building a Corpus on a Debate on Political Reform in Twitter
Authors: Mirko Lai, Daniela Virone, Cristina Bosco, Viviana Patti (Università di Torino, 2015)
Topic: Sentiment, Emotion, Irony, Hate

Abstract
The paper describes a project for the development of a French corpus for sentiment analysis focused on the texts generated by the participants to a debate about a political reform, i.e. the bill on homosexual wedding in France. Beyond the description of the data set, the paper shows the methodologies applied in the collection and annotation of data. The collection has been driven by the detection of the hashtag mainly used by the participants to the debate, while the annotation has been based on polarity but also extended with the target semantic areas involved in the debate.

Introduction
The recent trends in sentiment analysis are towards hybrid approaches or to computational semantics oriented frameworks where linguistic and pragmatic knowledge are encompassed for describing a global notion of communication. This notion includes e.g. context, themes, dialogical dynamics in order to detect the affective content even if it is not directly expressed by words, like, for instance, when the user exploits figurative language (e.g. irony, metaphor or hyperbole) or, in general, when the communicated content does not correspond to words meaning but depends also on other communicative behaviors. On this perspective, a particular interesting domain is related to the political debates and, in particular, in the specific form that such debates assumes in social media, which strongly differentiate them from other kinds of classical conversational contexts AUTHOR. In the last years social media, and in particular Twitter, have been used in electoral campaigns by different actors involved in the process: by campaign staffs in order to disseminate information, organize events; by the news media in order to inform and promote news content; and by voters to express and share political opinions. Therefore recently many studies focused on understanding the phenomenon, by studying the effect of this technology on the election outcomes AUTHOR, its possible use to gauge the political sentiment AUTHOR and the users' stance on controversial topics AUTHOR, or by studying the networks of communication in order to investigate the political polarization issue AUTHOR. This study contributes to this area by showing a methodology for the collection and annotation of a data set composed by texts from different media where a political debate has been developed. As a starting point of the project in this paper we will present a dataset of Twitter messages in French language about the reform ``Le Mariage Pour Tous" (Marriage for everyone, i.e. marriage for all), discussed in France in 2012 and 2013. The collection of th dataset has been driven by a hashtag, i.e. \#mariagepourtous, created to mark the messages about the debate on the reform, while the selection of tags to be annotated has been based on the detection and analysis of the semantic areas involved in users posts. The detection of these areas is the result of a set of analysis we applied on the corpus described in more details in AUTHOR. The paper is organized as follows. The next two sections respectively describe related works and the data set, showing the criteria and methodologies applied for the selection of data. Fourth section is in instead devoted to the annotation of collected data.

Conclusion
The paper presents a data-driven methodology for collecting and annotating corpora for sentiment analysis, which has been applied to a French corpus of a Twitter debate about a political reform. The collection is driven by a hashtag expoited by users expressing opinions of a controversial topic. The annotation is based on a set classical polarity labels, extended with tags for figurative language devices (i.e. irony) and for a few semantic areas detected in posts, intended as aspects of the reform on which users express their opinions. The investigation of further aspects and information sources that can be found in data, e.g. emojis, links and images, is matter of future work.