TWITTIRÒ: an Italian Twitter Corpus with a Multi-layered Annotation for Irony

Provided the difficulties that still affect a correct identification of irony within the context of Sentiment Analysis tasks, in this paper we describe the main issues emerged during the development of a novel resource for Italian annotated for irony. The project mainly consists in the application o...

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Autores principales:	Alessandra Teresa Cignarella, Cristina Bosco, Viviana Patti, Mirko Lai
Formato:	article
Lenguaje:	EN
Publicado:	Accademia University Press 2018
Materias:	Social Sciences H Computational linguistics. Natural language processing P98-98.5
Acceso en línea:	https://doaj.org/article/abce63f8e2e048babae28facf8e9e684
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id	oai:doaj.org-article:abce63f8e2e048babae28facf8e9e684
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spelling	oai:doaj.org-article:abce63f8e2e048babae28facf8e9e6842021-12-02T09:52:31ZTWITTIRÒ: an Italian Twitter Corpus with a Multi-layered Annotation for Irony2499-455310.4000/ijcol.502https://doaj.org/article/abce63f8e2e048babae28facf8e9e6842018-12-01T00:00:00Zhttp://journals.openedition.org/ijcol/502https://doaj.org/toc/2499-4553Provided the difficulties that still affect a correct identification of irony within the context of Sentiment Analysis tasks, in this paper we describe the main issues emerged during the development of a novel resource for Italian annotated for irony. The project mainly consists in the application on the Twitter corpus TWITTIRÒ of a multi-layered scheme for the fine-grained annotation of irony, as proposed in a multilingual setting and previously applied also on French and English datasets (Karoui et al. 2017). In applying the annotation on this corpus, we outline and discuss the issues and peculiarities emerged about the exploitation of the semantic scheme for Twitter textual messages in Italian, thus shedding some lights on the future directions that can be followed in the multilingual and cross-language perspective too. We present, in particular, an analysis of the annotation process and distribution of the labels of each layer involved in the scheme. This is supported by a discussion of the outcome of the annotation carried on by native Italian speakers in the development of the corpus. In particular, an in-depth discussion of the inter-annotator agreement and of the sources of disagreement is included. The result is a novel gold standard corpus for irony detection in Italian, which enriches the scenario of multilingual datasets available for this challenging task and is ready to be used as a benchmark in automatic irony detection experiments and evaluation campaigns.Alessandra Teresa CignarellaCristina BoscoViviana PattiMirko LaiAccademia University PressarticleSocial SciencesHComputational linguistics. Natural language processingP98-98.5ENIJCoL, Vol 4, Iss 2, Pp 25-43 (2018)
institution	DOAJ
collection	DOAJ
language	EN
topic	Social Sciences H Computational linguistics. Natural language processing P98-98.5
spellingShingle	Social Sciences H Computational linguistics. Natural language processing P98-98.5 Alessandra Teresa Cignarella Cristina Bosco Viviana Patti Mirko Lai TWITTIRÒ: an Italian Twitter Corpus with a Multi-layered Annotation for Irony
description	Provided the difficulties that still affect a correct identification of irony within the context of Sentiment Analysis tasks, in this paper we describe the main issues emerged during the development of a novel resource for Italian annotated for irony. The project mainly consists in the application on the Twitter corpus TWITTIRÒ of a multi-layered scheme for the fine-grained annotation of irony, as proposed in a multilingual setting and previously applied also on French and English datasets (Karoui et al. 2017). In applying the annotation on this corpus, we outline and discuss the issues and peculiarities emerged about the exploitation of the semantic scheme for Twitter textual messages in Italian, thus shedding some lights on the future directions that can be followed in the multilingual and cross-language perspective too. We present, in particular, an analysis of the annotation process and distribution of the labels of each layer involved in the scheme. This is supported by a discussion of the outcome of the annotation carried on by native Italian speakers in the development of the corpus. In particular, an in-depth discussion of the inter-annotator agreement and of the sources of disagreement is included. The result is a novel gold standard corpus for irony detection in Italian, which enriches the scenario of multilingual datasets available for this challenging task and is ready to be used as a benchmark in automatic irony detection experiments and evaluation campaigns.
format	article
author	Alessandra Teresa Cignarella Cristina Bosco Viviana Patti Mirko Lai
author_facet	Alessandra Teresa Cignarella Cristina Bosco Viviana Patti Mirko Lai
author_sort	Alessandra Teresa Cignarella
title	TWITTIRÒ: an Italian Twitter Corpus with a Multi-layered Annotation for Irony
title_short	TWITTIRÒ: an Italian Twitter Corpus with a Multi-layered Annotation for Irony
title_full	TWITTIRÒ: an Italian Twitter Corpus with a Multi-layered Annotation for Irony
title_fullStr	TWITTIRÒ: an Italian Twitter Corpus with a Multi-layered Annotation for Irony
title_full_unstemmed	TWITTIRÒ: an Italian Twitter Corpus with a Multi-layered Annotation for Irony
title_sort	twittirò: an italian twitter corpus with a multi-layered annotation for irony
publisher	Accademia University Press
publishDate	2018
url	https://doaj.org/article/abce63f8e2e048babae28facf8e9e684
work_keys_str_mv	AT alessandrateresacignarella twittiroanitaliantwittercorpuswithamultilayeredannotationforirony AT cristinabosco twittiroanitaliantwittercorpuswithamultilayeredannotationforirony AT vivianapatti twittiroanitaliantwittercorpuswithamultilayeredannotationforirony AT mirkolai twittiroanitaliantwittercorpuswithamultilayeredannotationforirony
_version_	1718397962981212160

TWITTIRÒ: an Italian Twitter Corpus with a Multi-layered Annotation for Irony

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