@InProceedings{shin-lee-choi:2017:WASSA2017,
  author    = {Shin, Bonggun  and  Lee, Timothy  and  Choi, Jinho D.},
  title     = {Lexicon Integrated CNN Models with Attention for Sentiment Analysis},
  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     = {149--158},
  abstract  = {With the advent of word embeddings, lexicons are no longer fully utilized for
	sentiment analysis although they still provide important features in the
	traditional setting. This paper introduces a novel approach to sentiment
	analysis that integrates lexicon embeddings and an attention mechanism into
	Convolutional Neural Networks. Our approach performs separate convolutions for
	word and lexicon embeddings and provides a global view of the document using
	attention. Our models are experimented on both the SemEval'16 Task 4 dataset
	and the Stanford Sentiment Treebank and show comparative or better results
	against the existing state-of-the-art systems. Our analysis shows that lexicon
	embeddings allow building high-performing models with much smaller word
	embeddings, and the attention mechanism effectively dims out noisy words for
	sentiment analysis.},
  url       = {http://www.aclweb.org/anthology/W17-5220}
}