@InProceedings{zhang-EtAl:2017:SemEval2,
  author    = {Zhang, Haowei  and  Wang, Jin  and  Zhang, Jixian  and  Zhang, Xuejie},
  title     = {YNU-HPCC at SemEval 2017 Task 4: Using A Multi-Channel CNN-LSTM Model for Sentiment 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     = {796--801},
  abstract  = {In this paper, we propose a multi-channel convolutional neural network-long
	short-term memory (CNN-LSTM) model that consists of two parts: multi-channel
	CNN and LSTM to analyze the sentiments of short English messages from Twitter.
	Un-like a conventional CNN, the proposed model applies a multi-channel strategy
	that uses several filters of different length to extract active local n-gram
	features in different scales. This information is then sequentially composed
	using LSTM. By combining both CNN and LSTM, we can consider both local
	information within tweets and long-distance dependency across tweets in the
	classification process. Officially released results show that our system
	outperforms the baseline algo-rithm.},
  url       = {http://www.aclweb.org/anthology/S17-2134}
}