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...
| Principais autores: | , , , , |
|---|---|
| Formato: | Artigo |
| Idioma: | English |
| Publicado em: |
Institute of Electrical and Electronics Engineers
2021
|
| Acesso em linha: | http://psasir.upm.edu.my/id/eprint/95009/1/Sarcasm%20detection%20using%20deep%20learning%20with%20contextual%20features.pdf |
| _version_ | 1825937500075458560 |
|---|---|
| author | Razali, Md Saifullah Doraisamy, Shyamala Abdul Halin, Alfian Lei, Ye Mohd Norowi, Noris |
| author_facet | Razali, Md Saifullah Doraisamy, Shyamala Abdul Halin, Alfian Lei, Ye Mohd Norowi, Noris |
| author_sort | Razali, Md Saifullah |
| collection | UPM |
| 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.. |
| first_indexed | 2024-03-06T11:00:27Z |
| format | Article |
| id | upm.eprints-95009 |
| institution | Universiti Putra Malaysia |
| language | English |
| last_indexed | 2024-03-06T11:00:27Z |
| publishDate | 2021 |
| publisher | Institute of Electrical and Electronics Engineers |
| record_format | dspace |
| spelling | 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 |
| spellingShingle | Razali, Md Saifullah Doraisamy, Shyamala Abdul Halin, Alfian Lei, Ye Mohd Norowi, Noris Sarcasm detection using deep learning with contextual features |
| title | 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_short | Sarcasm detection using deep learning with contextual features |
| title_sort | sarcasm detection using deep learning with contextual features |
| url | http://psasir.upm.edu.my/id/eprint/95009/1/Sarcasm%20detection%20using%20deep%20learning%20with%20contextual%20features.pdf |
| work_keys_str_mv | AT razalimdsaifullah sarcasmdetectionusingdeeplearningwithcontextualfeatures AT doraisamyshyamala sarcasmdetectionusingdeeplearningwithcontextualfeatures AT abdulhalinalfian sarcasmdetectionusingdeeplearningwithcontextualfeatures AT leiye sarcasmdetectionusingdeeplearningwithcontextualfeatures AT mohdnorowinoris sarcasmdetectionusingdeeplearningwithcontextualfeatures |