@InProceedings{elbeltagy-elkalamawy-soliman:2017:SemEval,
  author    = {El-Beltagy, Samhaa R.  and  El kalamawy, Mona  and  Soliman, Abu Bakr},
  title     = {NileTMRG at SemEval-2017 Task 4: Arabic Sentiment Analysis},
  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     = {790--795},
  abstract  = {This paper describes two systems that were used by the NileTMRG for addressing
	Arabic Sentiment Analysis as part of SemEval-2017, task 4. NileTMRG
	participated in three Arabic related subtasks which are: Subtask A (Message
	Polarity Classification), Subtask B (Topic-Based Message Polarity
	classification) and Subtask D  (Tweet quantification). For subtask A, we
	made use of NU’s sentiment analyzer which we augmented with a scored lexicon.
	For subtasks B and D, we used an ensemble of  three different classifiers. The
	first classifier was a convolutional neural network that used trained
	(word2vec) word embeddings. The second classifier consisted of a  MultiLayer
	Perceptron  while the third classifier was a Logistic regression model that
	takes the same input as the second classifier. Voting between the three
	classifiers was used to determine the final outcome.  In all three Arabic
	related tasks in which NileTMRG participated, the team ranked at number one.},
  url       = {http://www.aclweb.org/anthology/S17-2133}
}