Context-Driven Satire Detection With Deep Learning

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 (Yang et al., 2017), 0.90 (Zhang et al., 2016), and 0.88 (Rubin et al., 2016). The performance of each feature sets are also given as additional information. The main purpose of this work is to show that the combination of features extracted using supervised learning with the ones extracted manually can yield a good performance. It is also to open doors for other researchers to take into account the contextual meaning behind a figurative language type such as satire.

Automated summary

This work discusses the task of automatically detecting satire instances in short articles. It explores the extraction of optimal features using a deep learning architecture and contextual features. It demonstrates that combining feature sets can improve performance, with Logistic Regression identified as the best algorithm. The results outperform existing works in the same domain, highlighting the importance of considering the contextual meaning behind satire.