@inproceedings{abu-farha-magdy-2019-mazajak,
    title = "{M}azajak: An Online {A}rabic Sentiment Analyser",
    author = "Abu Farha, Ibrahim  and
      Magdy, Walid",
    editor = "El-Hajj, Wassim  and
      Belguith, Lamia Hadrich  and
      Bougares, Fethi  and
      Magdy, Walid  and
      Zitouni, Imed  and
      Tomeh, Nadi  and
      El-Haj, Mahmoud  and
      Zaghouani, Wajdi",
    booktitle = "Proceedings of the Fourth Arabic Natural Language Processing Workshop",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/iwcs-25-ingestion/W19-4621/",
    doi = "10.18653/v1/W19-4621",
    pages = "192--198",
    abstract = "Sentiment analysis (SA) is one of the most useful natural language processing applications. Literature is flooding with many papers and systems addressing this task, but most of the work is focused on English. In this paper, we present ``Mazajak'', an online system for Arabic SA. The system is based on a deep learning model, which achieves state-of-the-art results on many Arabic dialect datasets including SemEval 2017 and ASTD. The availability of such system should assist various applications and research that rely on sentiment analysis as a tool."
}