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Ensemble Learning for Irony Detection in Arabic Tweets

Muhammad Khalifa, Noura Hussein
Computer Science Department, Cairo University, Egypt & Computer Science Department, Benha University, Egypt

Abstract: In this paper, we describe and show the results of our 3 systems submitted for the Irony Detection in Arabic Tweets Shared Task at the Forum for Information Retrieval (FIRE 2019). We employ ensemble learning for this task through 3 different types of ensemble models, namely classical, deep and hybrid (that combines both). We extract types of features from the tweets including TF-IDF word n-gram features, topic modeling features, bag-of-words and sentiment features. Our submitted systems scored the top 3 places with our best system achieving 84.4 F1 points on the test set.

Keywords: Irony Detection, Ensemble Learning, Text Classification Ensemble Learning for Irony Detection in Arabic Tweets

DOI:https://doi.org/10.6025/jdp/2020/10/2/57-61

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