6533b7dcfe1ef96bd1271fa5
RESEARCH PRODUCT
A Novel Approach for Supporting Italian Satire Detection Through Deep Learning
Giosuè Lo BoscoDaniele SchicchiGabriella CasalinoAlfredo CuzzocreaMariano MaioranaGiovanni Pilatosubject
Settore ING-INF/05 - Sistemi Di Elaborazione Delle InformazioniSettore INF/01 - InformaticaSarcasmComputer scienceNatural language processingmedia_common.quotation_subjectSentiment analysisSatire detectionDeep learningContext (language use)Literal and figurative languageLinguisticsNewspaperPoliticsExaggerationComputational linguisticsmedia_commondescription
Satire is a way of criticizing people (or ideas) by ridiculing them on political, social, and morals topics often used to denounce political and societal problems, leveraging comedic devices such as parody exaggeration, incongruity, etc.etera. Detecting satire is one of the most challenging computational linguistics tasks, natural language processing, and social multimedia sentiment analysis. In particular, as satirical texts include figurative communication for expressing ideas/opinions concerning people, sentiment analysis systems may be negatively affected; therefore, satire should be adequately addressed to avoid such systems’ performance degradation. This paper tackles automatic satire detection through effective deep learning (DL) architecture that has been shown to be useful for addressing sarcasm/irony detection problems. We both trained and tested the system exploiting articles derived from two important satiric blogs, Lercio and IlFattoQuotidaino, and significant Italian newspapers. Experiments show an optimal performance achieved by the network capable of detecting satire in a context where it is not marked.
| year | journal | country | edition | language |
|---|---|---|---|---|
| 2021-01-01 |