Subjective Well-Being and Social Media: A Semantically Annotated Twitter Corpus on Fertility and Parenthood
Authors: Emilio Sulis, Cristina Bosco, Viviana Patti, Mirko Lai, Delia Irazù Hernàndez Farìas, Letizia Mencarini, Michele Mozzachiodi, Daniele Vignoli (Università di Torino, University of Valencia, Università Bocconi, Università di Firenze, 2016)
Topic: Sentiment, Emotion, Irony, Hate

Abstract
This article describes a Twitter corpus of social media contents in the Subjective Well-Being domain. A multi-layered manual annotation for exploring attitudes on fertility and parenthood has been applied. The corpus was further analysed by using sentiment and emotion lexicons in order to highlight relationships between the use of affective language and specific sub-topics in the domain. This analysis is useful to identify features for the development of an automatic tool for sentiment-related classification tasks in this domain. The gold standard is available to the community.

Introduction
The key research questions we address in this paper concern how subjective well-being drives fertility trends (and vice versa). We developed a Twitter Italian corpus annotated with a novel semantic annotation scheme for marking information not only about sentiment polarity, but also about the specific semantic areas/sub-topics which are the target of sentiment in the fertility-SWB domain. The relationship between big data and official statistics is increasingly a subject of attention AUTHOR. In this work we focus on Twitter data for two main reasons. First, Twitter individuals' opinions are posted spontaneously (not responding to a question) and often as a reaction to some emotional driven observation. Moreover, using Twitter we can incorporate additional measures of attitudes towards children and parenthood, with a wider geographical coverage than what is the case for traditional survey. Sentiment analysis in Twitter has been also used to monitor political sentiment AUTHOR, to extract critical information during times of mass emergency AUTHOR, or to analyse user stance in political debates on controversial topics AUTHOR. A comprehensive overview of sentiment analysis with annotated corpora is offered in AUTHOR. Focusing on Italian, among the existing resources we mention the Senti-TUT corpus AUTHOR and the TWITA corpus AUTHOR that were recently exploited in the SENTIment POLarity Classification (SENTIPOLC) shared task AUTHOR. The corpus described in this paper enriches the scenario of datasets available for Italian, enabling also a finer grained analysis of sentiment related phenomena in a novel domain related to parenthood and fertility.

Conclusion
The contribution of this paper is the exploration of opinions and semantic orientation about fertility and parenthood by scrutinizing about 3 million Italian tweets. This analysis is useful to identify features for the development of an automatic system to address automatic classification tasks in this domain. The corpus is available to the community. Its development constitutes a first step and a precondition to a further analysis that can be applied on such contents in order to extract, from semantically enriched data, measures of SWB constructed in an indirect way. This will hopefully improve our understanding of attitudes on fertility and parenthood. We are currently extending the corpus by exploring the very interesting debate around the ``Fertility Day's initiative" from the Italy's Minister of Health Beatrice Lorenzin, which had a remarkable echo on social media such as Twitter, with a substantial number of (also sarcastic) messages with hashtag \#fertilityday posted. The authors gratefully acknowledge financial support from the European Research Council under the European ERC Grant Agreement n. StG-313617 (SWELL-FER: Subjective Well-being and Fertility, P.I. Letizia Mencarini).