Context-Driven Satire Detection With Deep Learning

Autor: Md Saifullah Razali, Alfian Abdul Halin, Yang-Wai Chow, Noris Mohd Norowi, Shyamala Doraisamy

Publikováno v: IEEE Access, Vol 10, Pp 78780-78787 (2022)

This work discuss the task of automatically detecting satire instances in short articles. It is the study of extracting the most optimal features by using a deep learning architecture combined with carefully handcrafted contextual features. It is fou

Externí odkaz: https://doaj.org/article/b2852af49d754e99b9999c51d4a4ecb1

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Satire identification in Turkish news articles based on ensemble of classifiers

Autor: Mansur Alp Tocoglu, Aytuğ Onan

Publikováno v: Volume: 28, Issue: 2 1086-1106
Turkish Journal of Electrical Engineering and Computer Science

Social media and microblogging platforms generally contain elements of figurative and nonliteral language, including satire. The identification of figurative language is a fundamental task for sentiment analysis. It will not be possible to obtain sen

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::1b2ca9672ded58861bd353d0702249cb
https://doi.org/10.3906/elk-1907-11

Towards a deep-learning-based methodology for supporting satire detection

Autor: Cuzzocrea A., Lo Bosco G., Maiorana M., Pilato G., Schicchi D.

Publikováno v: DMSVIVA 2021: 27th International DMS Conference on Visualization and Visual Languages, pp. 92–96, Virtual, Pittsburgh, 29 June 2021-30 June 2021
info:cnr-pdr/source/autori:Cuzzocrea A.; Bosco G.L.; Maiorana M.; Pilato G.; Schicchi D./congresso_nome:DMSVIVA 2021: 27th International DMS Conference on Visualization and Visual Languages/congresso_luogo:Virtual, Pittsburgh/congresso_data:29 June 2021-30 June 2021/anno:2021/pagina_da:92/pagina_a:96/intervallo_pagine:92–96

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 explo

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=dedup_wf_001::c9b0c0f55d1c1270d95fda7fe4b0b5f0
http://www.cnr.it/prodotto/i/458858

FreSaDa: A French Satire Data Set for Cross-Domain Satire Detection

Autor: Adrian-Gabriel Chifu, Radu Tudor Ionescu

Publikováno v: The International Joint Conference on Neural Network, IJCNN 2021
The International Joint Conference on Neural Network, IJCNN 2021, Jul 2021, Virtual Event, United States
IJCNN

In this paper, we introduce FreSaDa, a French Satire Data Set, which is composed of 11,570 articles from the news domain. In order to avoid reporting unreasonably high accuracy rates due to the learning of characteristics specific to publication sour

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::b4d9a836451bcee80fe39ffeb7f78eaa