@InProceedings{mulki-EtAl:2017:SemEval,
  author    = {Mulki, Hala  and  Haddad, Hatem  and  Gridach, Mourad  and  Babao\u{g}lu, Ismail},
  title     = {Tw-StAR at SemEval-2017 Task 4: Sentiment Classification of Arabic Tweets},
  booktitle = {Proceedings of the 11th International Workshop on Semantic Evaluation (SemEval-2017)},
  month     = {August},
  year      = {2017},
  address   = {Vancouver, Canada},
  publisher = {Association for Computational Linguistics},
  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.},
  url       = {http://www.aclweb.org/anthology/S17-2110}
}