@InProceedings{baly-EtAl:2017:SemEval,
  author    = {Baly, Ramy  and  Badaro, Gilbert  and  Hamdi, Ali  and  Moukalled, Rawan  and  Aoun, Rita  and  El-Khoury, Georges  and  Al Sallab, Ahmad  and  Hajj, Hazem  and  Habash, Nizar  and  Shaban, Khaled  and  El-Hajj, Wassim},
  title     = {OMAM at SemEval-2017 Task 4: Evaluation of English State-of-the-Art Sentiment Analysis Models for Arabic and a New Topic-based Model},
  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     = {603--610},
  abstract  = {While sentiment analysis in English has achieved significant progress, it
	remains a challenging task in Arabic given the rich morphology of the language.
	It becomes more challenging when applied to Twitter data that comes with
	additional sources of noise including dialects, misspellings, grammatical
	mistakes, code switching and the use of non-textual objects to express
	sentiments. This paper describes the “OMAM” systems that we developed as
	part of SemEval-2017 task 4. We evaluate English state-of-the-art methods on
	Arabic tweets for subtask A. As for the remaining subtasks, we introduce a
	topic-based approach that accounts for topic specificities by predicting topics
	or domains of upcoming tweets, and then using this information to predict their
	sentiment. Results indicate that applying the English state-of-the-art method
	to Arabic has achieved solid results without significant enhancements.
	Furthermore, the topic-based method ranked 1st in subtasks C and E, and 2nd in
	subtask D.},
  url       = {http://www.aclweb.org/anthology/S17-2099}
}