@inproceedings{mulki-etal-2017-tw,
    title = "Tw-{S}t{AR} at {S}em{E}val-2017 Task 4: Sentiment Classification of {A}rabic Tweets",
    author = "Mulki, Hala  and
      Haddad, Hatem  and
      Gridach, Mourad  and
      Babaoglu, Ismail",
    editor = "Bethard, Steven  and
      Carpuat, Marine  and
      Apidianaki, Marianna  and
      Mohammad, Saif M.  and
      Cer, Daniel  and
      Jurgens, David",
    booktitle = "Proceedings of the 11th International Workshop on Semantic Evaluation ({S}em{E}val-2017)",
    month = aug,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/fix-sig-urls/S17-2110/",
    doi = "10.18653/v1/S17-2110",
    pages = "664--669",
    abstract = "In this paper, we present our contribution in SemEval 2017 international workshop. We have tackled task 4 entitled ``Sentiment analysis in Twitter'', specifically subtask 4A-Arabic. We propose two Arabic sentiment classification models implemented using supervised and unsupervised learning strategies. In both models, Arabic tweets were preprocessed first then various schemes of bag-of-N-grams were extracted to be used as features. The final submission was selected upon the best performance achieved by the supervised learning-based model. However, the results obtained by the unsupervised learning-based model are considered promising and evolvable if more rich lexica are adopted in further work."
}