@InProceedings{wang-EtAl:2017:SemEval,
  author    = {Wang, Ming  and  Chu, Biao  and  Liu, Qingxun  and  Zhou, Xiaobing},
  title     = {YNUDLG at SemEval-2017 Task 4: A GRU-SVM Model for Sentiment Classification and Quantification in Twitter},
  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     = {713--717},
  abstract  = {Sentiment analysis is one of the central issues in Natural Language Processing
	and has become more and more important in many fields. Typical sentiment
	analysis classifies the sentiment of sentences into several discrete classes
	(e.g.,positive or negative). In this paper we describe our deep learning
	system(combining GRU and SVM) to solve both two-, three- and five- tweet
	polarity classifications. We first trained a gated recurrent neural network
	using pre-trained word embeddings, then we extracted features from GRU layer
	and input these features into support vector machine to fulfill both the
	classification and quantification subtasks. The proposed approach achieved
	37th, 19th, and 14rd places in subtasks A, B and C, respectively.},
  url       = {http://www.aclweb.org/anthology/S17-2119}
}