Irony and Sarcasm: Corpus Generation and Analysis Using Crowdsourcing

Elena Filatova


Abstract
The ability to reliably identify sarcasm and irony in text can improve the performance of many Natural Language Processing (NLP) systems including summarization, sentiment analysis, etc. The existing sarcasm detection systems have focused on identifying sarcasm on a sentence level or for a specific phrase. However, often it is impossible to identify a sentence containing sarcasm without knowing the context. In this paper we describe a corpus generation experiment where we collect regular and sarcastic Amazon product reviews. We perform qualitative and quantitative analysis of the corpus. The resulting corpus can be used for identifying sarcasm on two levels: a document and a text utterance (where a text utterance can be as short as a sentence and as long as a whole document).
Anthology ID:
L12-1386
Volume:
Proceedings of the Eighth International Conference on Language Resources and Evaluation (LREC'12)
Month:
May
Year:
2012
Address:
Istanbul, Turkey
Venue:
LREC
SIG:
Publisher:
European Language Resources Association (ELRA)
Note:
Pages:
392–398
Language:
URL:
http://www.lrec-conf.org/proceedings/lrec2012/pdf/661_Paper.pdf
DOI:
Bibkey:
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PDF:
http://www.lrec-conf.org/proceedings/lrec2012/pdf/661_Paper.pdf