| 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; Arabic NLP; Ensemble Based Classifiers; SVM |
| journal | Forum of Information Retrieval Evaluation (FIRE2019) |
| volume | Not Available |
| issue | Not Available |
| pages | Not Available |
| publisher | Not Available |
| Local/International | International |
| Paper Link | http://ceur-ws.org/Vol-2517/T4-3.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 Detec- tion 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