| publication name | BENHA@ IDAT: Improving Irony Detection in Arabic Tweets using Ensemble Approach. |
|---|---|
| Authors | Hamada A Nayel, Walaa Medhat, Metwally Rashad |
| year | 2019 |
| keywords | Irony Detection, NLP |
| journal | FIRE (Working Notes) |
| volume | Not Available |
| issue | Not Available |
| pages | 401-408 |
| publisher | Not Available |
| Local/International | International |
| Paper Link | https://www.researchgate.net/profile/Hamada_Nayel2/publication/337944997_BENHAIDAT_Improving_Irony_Detection_in_Arabic_Tweets_using_Ensemble_Approach/links/5df70595299bf10bc35f0a02/BENHAIDAT-Improving-Irony-Detection-in-Arabic-Tweets-using-Ensemble-Approach.pdf |
| Full paper | download |
| Supplementary materials | Not Available |
Abstract
This paper describes the methods and experiments that have been used in the development of our model submitted to Irony Detection for Arabic Tweets shared task. We submitted three runs based on our model using Support Vector Machines (SVM), Linear and Ensemble classifiers. Bag-of-Words with range of n-grams model have been used for feature extraction. Our submissions achieved accuracies of 82.1%, 81.6% and 81.1% for ensemble based, SVM and linear classifiers respectively.