@InProceedings{yang-tseng-chen:2017:SemEval,
  author    = {Yang, Tzu-Hsuan  and  Tseng, Tzu-Hsuan  and  Chen, Chia-Ping},
  title     = {deepSA at SemEval-2017 Task 4: Interpolated Deep Neural Networks for Sentiment Analysis 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     = {616--620},
  abstract  = {In this paper, we describe our system implementation for sentiment analysis in
	Twitter. This system combines two models based on deep neural networks, namely
	a convolutional neural network (CNN) and a long short-term memory (LSTM)
	recurrent neural network, through interpolation. Distributed representation of
	words as vectors are input to the system, and the output is a sentiment class.
	The neural network models are trained exclusively with the data sets provided
	by the organizers of SemEval-2017 Task 4 Subtask A. Overall, this system has
	achieved 0.618 for the average recall rate, 0.587 for the average F1 score, and
	0.618 for accuracy.},
  url       = {http://www.aclweb.org/anthology/S17-2101}
}