@InProceedings{hamdan:2017:SemEval,
  author    = {Hamdan, Hussam},
  title     = {Senti17 at SemEval-2017 Task 4: Ten Convolutional Neural Network Voters for Tweet Polarity Classification},
  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     = {700--703},
  abstract  = {This paper presents Senti17 system which uses ten convolutional neural networks
	(Con- vNet) to assign a sentiment label to a tweet. The network consists of a
	convolutional layer followed by a fully-connected layer and a Soft- max on top.
	Ten instances of this network are initialized with the same word embeddings  as
	inputs but with different initializations for the network weights. We combine
	the results of all instances by selecting the sentiment label given by the
	majority of the ten voters. This system is ranked fourth in SemEval-2017 Task4
	over 38 systems with 67.4\% average recall.},
  url       = {http://www.aclweb.org/anthology/S17-2116}
}