Ensemble Classification Approach for Sarcasm Detection
Cognitive science is a technology which focuses on analyzing the human brain using the application of DM. The databases are utilized to gather and store the large volume of data. The authenticated information is extracted using measures. This research work is based on detecting the sarcasm from the...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2021-01-01
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| Series: | Behavioural Neurology |
| Online Access: | http://dx.doi.org/10.1155/2021/9731519 |
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| _version_ | 1832567928701059072 |
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| author | Jyoti Godara Isha Batra Rajni Aron Mohammad Shabaz |
| author_facet | Jyoti Godara Isha Batra Rajni Aron Mohammad Shabaz |
| author_sort | Jyoti Godara |
| collection | DOAJ |
| description | Cognitive science is a technology which focuses on analyzing the human brain using the application of DM. The databases are utilized to gather and store the large volume of data. The authenticated information is extracted using measures. This research work is based on detecting the sarcasm from the text data. This research work introduces a scheme to detect sarcasm based on PCA algorithm, K-means algorithm, and ensemble classification. The four ensemble classifiers are designed with the objective of detecting the sarcasm. The first ensemble classification algorithm (SKD) is the combination of SVM, KNN, and decision tree. In the second ensemble classifier (SLD), SVM, logistic regression, and decision tree classifiers are combined for the sarcasm detection. In the third ensemble model (MLD), MLP, logistic regression, and decision tree are combined, and the last one (SLM) is the combination of MLP, logistic regression, and SVM. The proposed model is implemented in Python and tested on five datasets of different sizes. The performance of the models is tested with regard to various metrics. |
| format | Article |
| id | doaj-art-3c196a2ab14d435f8804188fd76ad138 |
| institution | Kabale University |
| issn | 1875-8584 |
| language | English |
| publishDate | 2021-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Behavioural Neurology |
| spelling | doaj-art-3c196a2ab14d435f8804188fd76ad1382025-02-03T01:00:08ZengWileyBehavioural Neurology1875-85842021-01-01202110.1155/2021/9731519Ensemble Classification Approach for Sarcasm DetectionJyoti Godara0Isha Batra1Rajni Aron2Mohammad Shabaz3Department of Computer Science and EngineeringDepartment of Computer Science and EngineeringSVKM’s Narsee Monjee Institute of Management Studies (NMIMS)Department of Computer Science EngineeringCognitive science is a technology which focuses on analyzing the human brain using the application of DM. The databases are utilized to gather and store the large volume of data. The authenticated information is extracted using measures. This research work is based on detecting the sarcasm from the text data. This research work introduces a scheme to detect sarcasm based on PCA algorithm, K-means algorithm, and ensemble classification. The four ensemble classifiers are designed with the objective of detecting the sarcasm. The first ensemble classification algorithm (SKD) is the combination of SVM, KNN, and decision tree. In the second ensemble classifier (SLD), SVM, logistic regression, and decision tree classifiers are combined for the sarcasm detection. In the third ensemble model (MLD), MLP, logistic regression, and decision tree are combined, and the last one (SLM) is the combination of MLP, logistic regression, and SVM. The proposed model is implemented in Python and tested on five datasets of different sizes. The performance of the models is tested with regard to various metrics.http://dx.doi.org/10.1155/2021/9731519 |
| spellingShingle | Jyoti Godara Isha Batra Rajni Aron Mohammad Shabaz Ensemble Classification Approach for Sarcasm Detection Behavioural Neurology |
| title | Ensemble Classification Approach for Sarcasm Detection |
| title_full | Ensemble Classification Approach for Sarcasm Detection |
| title_fullStr | Ensemble Classification Approach for Sarcasm Detection |
| title_full_unstemmed | Ensemble Classification Approach for Sarcasm Detection |
| title_short | Ensemble Classification Approach for Sarcasm Detection |
| title_sort | ensemble classification approach for sarcasm detection |
| url | http://dx.doi.org/10.1155/2021/9731519 |
| work_keys_str_mv | AT jyotigodara ensembleclassificationapproachforsarcasmdetection AT ishabatra ensembleclassificationapproachforsarcasmdetection AT rajniaron ensembleclassificationapproachforsarcasmdetection AT mohammadshabaz ensembleclassificationapproachforsarcasmdetection |