Ensemble Learning for Irony Detection in Arabic Tweets
Muhammad Khalifa, Noura Hussein
Journal of Data Processing, Vol. 10, No. 2, 2020.
DOI: https://doi.org/10.6025/jdp/2020/10/2/57-61
Link: https://www.dline.info/jdp/fulltext/v10n2/jdpv10n2_3.pdf
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.