@inproceedings{gaanoun-benelallam-2021-sarcasm,
    title = "Sarcasm and Sentiment Detection in {A}rabic language A Hybrid Approach Combining Embeddings and Rule-based Features",
    author = "Gaanoun, Kamel  and
      Benelallam, Imade",
    editor = "Habash, Nizar  and
      Bouamor, Houda  and
      Hajj, Hazem  and
      Magdy, Walid  and
      Zaghouani, Wajdi  and
      Bougares, Fethi  and
      Tomeh, Nadi  and
      Abu Farha, Ibrahim  and
      Touileb, Samia",
    booktitle = "Proceedings of the Sixth Arabic Natural Language Processing Workshop",
    month = apr,
    year = "2021",
    address = "Kyiv, Ukraine (Virtual)",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-emnlp/2021.wanlp-1.45/",
    pages = "351--356",
    abstract = "This paper presents the ArabicProcessors team{'}s system designed for sarcasm (subtask 1) and sentiment (subtask 2) detection shared task. We created a hybrid system by combining rule-based features and both static and dynamic embeddings using transformers and deep learning. The system{'}s architecture is an ensemble of Naive bayes, MarBERT and Mazajak embedding. This process scored an F1-score of 51{\%} on sarcasm and 71{\%} for sentiment detection."
}