This paper describes an approach for supporting 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 IlFattoQuotidiano, and significant Italian newspapers.
Cuzzocrea A., Lo Bosco G., Maiorana M., Pilato G., Schicchi D. (2021). Towards a deep-learning-based methodology for supporting satire detection. In Proceedings - DMSVIVA 2021: 27th International DMS Conference on Visualization and Visual Languages (pp. 92-96). Knowledge Systems Institute Graduate School, KSI Research Inc. [10.18293/DMSVIVA2021-016].
Towards a deep-learning-based methodology for supporting satire detection
Lo Bosco G.;Pilato G.;Schicchi D.
2021-01-01
Abstract
This paper describes an approach for supporting 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 IlFattoQuotidiano, and significant Italian newspapers.
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