@InProceedings{zhang-EtAl:2017:WASSA2017,
  author    = {Zhang, You  and  Yuan, Hang  and  Wang, Jin  and  Zhang, Xuejie},
  title     = {YNU-HPCC at EmoInt-2017: Using a CNN-LSTM Model for Sentiment Intensity Prediction},
  booktitle = {Proceedings of the 8th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis},
  month     = {September},
  year      = {2017},
  address   = {Copenhagen, Denmark},
  publisher = {Association for Computational Linguistics},
  pages     = {200--204},
  abstract  = {In this paper, we present a system that uses a convolutional neural network
	with long short-term memory (CNN-LSTM) model to complete the task. The CNN-LSTM
	model has two combined parts: CNN extracts local n-gram features within tweets
	and LSTM composes the features to capture long-distance dependency across
	tweets. Additionally, we used other three models (CNN, LSTM, BiLSTM) as
	baseline algorithms. Our introduced model showed good performance in the
	experimental results.},
  url       = {http://www.aclweb.org/anthology/W17-5227}
}