@article{3002,
  author = {Muhammad Khalifa, Noura Hussein},
  title = {Ensemble Learning for Irony Detection in Arabic Tweets},
  journal = {Journal of Data Processing},
  year = {2020},
  volume = {10},
  number = {2},
  doi = {https://doi.org/10.6025/jdp/2020/10/2/57-61},
  url = {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.},
}