@InProceedings{nguyen-nguyen:2017:I17-1,
  author    = {Nguyen, Huy-Thanh  and  Nguyen, Minh-Le},
  title     = {Sentence Modeling with Deep Neural Architecture using Lexicon and Character Attention Mechanism for Sentiment Classification},
  booktitle = {Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 1: Long Papers)},
  month     = {November},
  year      = {2017},
  address   = {Taipei, Taiwan},
  publisher = {Asian Federation of Natural Language Processing},
  pages     = {536--544},
  abstract  = {Tweet-level sentiment classification in Twitter social networking has many
	challenges: exploiting syntax, semantic, sentiment, and context in tweets. To
	address these problems, we propose a novel approach to sentiment analysis that
	uses lexicon features for building lexicon embeddings (LexW2Vs) and generates
	character attention vectors (CharAVs) by using a Deep Convolutional Neural
	Network (DeepCNN). Our approach integrates LexW2Vs and CharAVs with continuous
	word embeddings (ContinuousW2Vs) and dependency-based word embeddings
	(DependencyW2Vs) simultaneously in order to increase information for each word
	into a Bidirectional Contextual Gated Recurrent Neural Network (Bi-CGRNN). We
	evaluate our model on two Twitter sentiment classification datasets.
	Experimental results show that our model can improve the classification
	accuracy of sentence-level sentiment analysis in Twitter social networking.},
  url       = {http://www.aclweb.org/anthology/I17-1054}
}