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"Context-Driven Satire Detection with Deep Learning" by Md Saifullah Razali, Alfian Abdul Halin et al.

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 found that a few sets can perform well when they are used independently, but the others not so much. However, even the latter sets become very useful after the combination process with the former sets. This shows that each of the feature sets are significant. Finally, the combined feature sets undergoes the classification using well-known machine learning classification algorithms. The best algorithm for this task is found to be Logistic Regression. The outcome of all the experiments are good in all the metrics used. The result comparison to existing works in the same domain shows that the proposed method is slightly better with 0.94 in terms of F1-measure, while existing works managed to obtain 0.91 [1], 0.90 [2] and 0.88 [3]. The performance ....

Convolutional Neural Networks , Deep Learning , Feature Extraction , Machine Learning Algorithms , Natural Language Processing , Atire Detection , Support Vector Machines , Task Analysis ,