BENHA@IDAT: Improving irony detection in Arabic tweets using ensemble approach

Hamada A. Nayel; Walaa Medhat; Metwally Rashad

BENHA@IDAT: Improving irony detection in Arabic tweets using ensemble approach

Hamada A. Nayel
, Walaa Medhat
, Metwally Rashad

Benha University

Research output: Contribution to journal › Conference article › peer-review

16 Scopus citations

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.

Original language	English
Pages (from-to)	401-408
Number of pages	8
Journal	CEUR Workshop Proceedings
Volume	2517
State	Published - 2019
Externally published	Yes
Event	11th Forum for Information Retrieval Evaluation, FIRE 2019 - Kolkata, India Duration: 12 Dec 2019 → 15 Dec 2019

Keywords

Arabic NLP
Ensemble Based Classifiers
Irony Detection
SVM

Cite this

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title = "BENHA@IDAT: Improving irony detection in Arabic tweets using ensemble approach",

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.",

keywords = "Arabic NLP, Ensemble Based Classifiers, Irony Detection, SVM",

author = "Nayel, \{Hamada A.\} and Walaa Medhat and Metwally Rashad",

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T2 - 11th Forum for Information Retrieval Evaluation, FIRE 2019

AU - Nayel, Hamada A.

AU - Medhat, Walaa

AU - Rashad, Metwally

PY - 2019

Y1 - 2019

N2 - 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.

AB - 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.

KW - Arabic NLP

KW - Ensemble Based Classifiers

KW - Irony Detection

KW - SVM

UR - https://www.scopus.com/pages/publications/85076877749

M3 - Conference article

AN - SCOPUS:85076877749

SN - 1613-0073

VL - 2517

SP - 401

EP - 408

JO - CEUR Workshop Proceedings

JF - CEUR Workshop Proceedings

Y2 - 12 December 2019 through 15 December 2019

ER -

BENHA@IDAT: Improving irony detection in Arabic tweets using ensemble approach

Abstract

Keywords

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