Subjective Well-Being and Social Media. A Semantically Annotated Twitter Corpus on Fertility and Parenthood

English. This article describes a Twitter corpus of social media contents in the Subjective Well-Being domain. A multilayered 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. Italiano. L’articolo descrive la creazione di un corpus tratto da Twitter sui temi del Subjective Well-Being, fertilità e genitorialità. Un’analisi lessicale ha mostrato il legame tra l’uso di linguaggio affettivo e specifiche categorie di messaggi. Questo esame è utile per se e per l’addestramento di sistemi di classificazione automatica sul dominio. Il gold standard è disponibile su

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

[2]  Joel D. Martin,et al.  Sentiment, emotion, purpose, and style in electoral tweets , 2015, Inf. Process. Manag..

[3]  Malvina Nissim,et al.  Semantic Aspects in Sentiment Analysis , 2017 .

[4]  Preslav Nakov,et al.  SemEval-2016 Task 4: Sentiment Analysis in Twitter , 2016, *SEMEVAL.

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

[6]  Cristina Bosco,et al.  Annotating Sentiment and Irony in the Online Italian Political Debate on #labuonascuola , 2016, LREC.

[7]  Christopher M. Danforth,et al.  The Geography of Happiness: Connecting Twitter Sentiment and Expression, Demographics, and Objective Characteristics of Place , 2013, PloS one.

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

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

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

[11]  Paolo Rosso,et al.  Irony Detection in Twitter , 2016, ACM Trans. Internet Techn..

[12]  Bing Liu,et al.  Mining and summarizing customer reviews , 2004, KDD.

[13]  Deirdre Wilson,et al.  The pragmatics of verbal irony: Echo or pretence? , 2006 .

[14]  Paolo Rosso,et al.  Figurative messages and affect in Twitter: Differences between #irony, #sarcasm and #not , 2016, Knowl. Based Syst..

[15]  Cristina Bosco,et al.  Tweeting and Being Ironic in the Debate about a Political Reform: the French Annotated Corpus TWitter-MariagePourTous , 2016, LREC.

[16]  Ingmar Weber,et al.  Demographic research with non-representative internet data , 2015 .

[17]  Leysia Palen,et al.  Natural Language Processing to the Rescue? Extracting "Situational Awareness" Tweets During Mass Emergency , 2011, ICWSM.

[18]  Saif Mohammad,et al.  CROWDSOURCING A WORD–EMOTION ASSOCIATION LEXICON , 2013, Comput. Intell..

[19]  Christian Reimsbach-Kounatze.  The Proliferation of "Big Data" and Implications for Official Statistics and Statistical Agencies , 2015 .

[20]  Davide Buscaldi,et al.  Sentiment Analysis on Microblogs for Natural Disasters Management: a Study on the 2014 Genoa Floodings , 2015, WWW.

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

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

[23]  Manuela Sanguinetti,et al.  Exploring Sentiment in Social Media and Official Statistics: a General Framework , 2015, ESSEM@AAMAS.

[24]  Finn Årup Nielsen,et al.  A New ANEW: Evaluation of a Word List for Sentiment Analysis in Microblogs , 2011, #MSM.

[25]  James W. Pennebaker,et al.  Linguistic Inquiry and Word Count (LIWC2007) , 2007 .