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...
| Main Authors: | , , , , |
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| Format: | Article |
| Published: |
IEEE
2022
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| Online Access: | http://psasir.upm.edu.my/id/eprint/100800/ |
| _version_ | 1848863417734004736 |
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| author | Razali, Md Saifullah Abdul Halin, Alfian Chow, Yang-Wai Mohd Norowi, Noris Doraisamy, Shyamala |
| author_facet | Razali, Md Saifullah Abdul Halin, Alfian Chow, Yang-Wai Mohd Norowi, Noris Doraisamy, Shyamala |
| author_sort | Razali, Md Saifullah |
| building | UPM Institutional Repository |
| collection | Online Access |
| description | 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. |
| first_indexed | 2025-11-15T13:32:35Z |
| format | Article |
| id | upm-100800 |
| institution | Universiti Putra Malaysia |
| institution_category | Local University |
| last_indexed | 2025-11-15T13:32:35Z |
| publishDate | 2022 |
| publisher | IEEE |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | upm-1008002023-08-23T03:25:37Z http://psasir.upm.edu.my/id/eprint/100800/ Context-driven satire detection with deep learning Razali, Md Saifullah Abdul Halin, Alfian Chow, Yang-Wai Mohd Norowi, Noris Doraisamy, Shyamala 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. IEEE 2022-07-27 Article PeerReviewed Razali, Md Saifullah and Abdul Halin, Alfian and Chow, Yang-Wai and Mohd Norowi, Noris and Doraisamy, Shyamala (2022) Context-driven satire detection with deep learning. IEEE Access, 10. 78780 - 78787. ISSN 2169-3536 https://ieeexplore.ieee.org/document/9841563 10.1109/ACCESS.2022.3194119 |
| spellingShingle | Razali, Md Saifullah Abdul Halin, Alfian Chow, Yang-Wai Mohd Norowi, Noris Doraisamy, Shyamala Context-driven satire detection with deep learning |
| title | Context-driven satire detection with deep learning |
| title_full | Context-driven satire detection with deep learning |
| title_fullStr | Context-driven satire detection with deep learning |
| title_full_unstemmed | Context-driven satire detection with deep learning |
| title_short | Context-driven satire detection with deep learning |
| title_sort | context-driven satire detection with deep learning |
| url | http://psasir.upm.edu.my/id/eprint/100800/ http://psasir.upm.edu.my/id/eprint/100800/ http://psasir.upm.edu.my/id/eprint/100800/ |