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Sarcasm detection using deep learning with contextual features

Our work focuses on detecting sarcasm in tweets using deep learning extracted features combined with contextual handcrafted features. A feature set is extracted from a Convolutional Neural Network (CNN) architecture before it is combined with carefully handcrafted feature sets. These handcrafted fea...

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Main Authors: Razali, Md Saifullah, Doraisamy, Shyamala, Abdul Halin, Alfian, Lei, Ye, Mohd Norowi, Noris
Format: Article
Language:English
Published: Institute of Electrical and Electronics Engineers 2021
Online Access:http://psasir.upm.edu.my/id/eprint/95009/1/Sarcasm%20detection%20using%20deep%20learning%20with%20contextual%20features.pdf
http://psasir.upm.edu.my/id/eprint/95009/
https://ieeexplore.ieee.org/document/9420094
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spelling my.upm.eprints.950092023-01-04T08:20:19Z http://psasir.upm.edu.my/id/eprint/95009/ Sarcasm detection using deep learning with contextual features Razali, Md Saifullah Doraisamy, Shyamala Abdul Halin, Alfian Lei, Ye Mohd Norowi, Noris Our work focuses on detecting sarcasm in tweets using deep learning extracted features combined with contextual handcrafted features. A feature set is extracted from a Convolutional Neural Network (CNN) architecture before it is combined with carefully handcrafted feature sets. These handcrafted feature sets are created based on their respective contextual explanations. Each feature sets are specifically designed for the sole task of sarcasm detection. The objective is to find the most optimal features. Some sets are good to go even when it is used in independence. Other sets are not significant without any combination. The results of the experiments are positive in terms of Accuracy, Precision, Recall and F1-measure. The combination of features is classified using a few machine learning techniques for comparison purposes. Logistic Regression is found to be the best classification algorithm for this task. Furthermore, result comparison to recent works and the performance of each feature set are also shown as additional information.. Institute of Electrical and Electronics Engineers 2021 Article PeerReviewed text en http://psasir.upm.edu.my/id/eprint/95009/1/Sarcasm%20detection%20using%20deep%20learning%20with%20contextual%20features.pdf Razali, Md Saifullah and Doraisamy, Shyamala and Abdul Halin, Alfian and Lei, Ye and Mohd Norowi, Noris (2021) Sarcasm detection using deep learning with contextual features. IEEE Access, 9. 68609 - 68618. ISSN 2169-3536 https://ieeexplore.ieee.org/document/9420094 10.1109/ACCESS.2021.3076789
institution Universiti Putra Malaysia
building UPM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Putra Malaysia
content_source UPM Institutional Repository
url_provider http://psasir.upm.edu.my/
language English
description Our work focuses on detecting sarcasm in tweets using deep learning extracted features combined with contextual handcrafted features. A feature set is extracted from a Convolutional Neural Network (CNN) architecture before it is combined with carefully handcrafted feature sets. These handcrafted feature sets are created based on their respective contextual explanations. Each feature sets are specifically designed for the sole task of sarcasm detection. The objective is to find the most optimal features. Some sets are good to go even when it is used in independence. Other sets are not significant without any combination. The results of the experiments are positive in terms of Accuracy, Precision, Recall and F1-measure. The combination of features is classified using a few machine learning techniques for comparison purposes. Logistic Regression is found to be the best classification algorithm for this task. Furthermore, result comparison to recent works and the performance of each feature set are also shown as additional information..
format Article
author Razali, Md Saifullah
Doraisamy, Shyamala
Abdul Halin, Alfian
Lei, Ye
Mohd Norowi, Noris
spellingShingle Razali, Md Saifullah
Doraisamy, Shyamala
Abdul Halin, Alfian
Lei, Ye
Mohd Norowi, Noris
Sarcasm detection using deep learning with contextual features
author_facet Razali, Md Saifullah
Doraisamy, Shyamala
Abdul Halin, Alfian
Lei, Ye
Mohd Norowi, Noris
author_sort Razali, Md Saifullah
title Sarcasm detection using deep learning with contextual features
title_short Sarcasm detection using deep learning with contextual features
title_full Sarcasm detection using deep learning with contextual features
title_fullStr Sarcasm detection using deep learning with contextual features
title_full_unstemmed Sarcasm detection using deep learning with contextual features
title_sort sarcasm detection using deep learning with contextual features
publisher Institute of Electrical and Electronics Engineers
publishDate 2021
url http://psasir.upm.edu.my/id/eprint/95009/1/Sarcasm%20detection%20using%20deep%20learning%20with%20contextual%20features.pdf
http://psasir.upm.edu.my/id/eprint/95009/
https://ieeexplore.ieee.org/document/9420094
_version_ 1754531206033047552
score 13.211869

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