@InProceedings{wang-jiang-luo:2016:COLING,
  author    = {Wang, Xingyou  and  Jiang, Weijie  and  Luo, Zhiyong},
  title     = {Combination of Convolutional and Recurrent Neural Network for Sentiment Analysis of Short Texts},
  booktitle = {Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers},
  month     = {December},
  year      = {2016},
  address   = {Osaka, Japan},
  publisher = {The COLING 2016 Organizing Committee},
  pages     = {2428--2437},
  abstract  = {Sentiment analysis of short texts is challenging because of the limited
	contextual information they usually contain. In recent years, deep learning
	models such as convolutional neural networks (CNNs) and recurrent neural
	networks (RNNs) have been applied to text sentiment analysis with comparatively
	remarkable results. In this paper, we describe a jointed CNN and RNN
	architecture, taking advantage of the coarse-grained local features generated
	by CNN and long-distance dependencies learned via RNN for sentiment analysis of
	short texts. Experimental results show an obvious improvement upon the
	state-of-the-art on three benchmark corpora, MR, SST1 and SST2, with 82.28%,
	51.50% and 89.95% accuracy, respectively.},
  url       = {http://aclweb.org/anthology/C16-1229}
}