@InProceedings{wang-zhang-liu:2017:EMNLP2017,
  author    = {Wang, Yasheng  and  Zhang, Yang  and  Liu, Bing},
  title     = {Sentiment Lexicon Expansion Based on Neural PU Learning, Double Dictionary Lookup, and Polarity Association},
  booktitle = {Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing},
  month     = {September},
  year      = {2017},
  address   = {Copenhagen, Denmark},
  publisher = {Association for Computational Linguistics},
  pages     = {553--563},
  abstract  = {Although many sentiment lexicons in different languages exist, most are not
	comprehensive. In a recent sentiment analysis application, we used a large
	Chinese sentiment lexicon and found that it missed a large number of sentiment
	words in social media. This prompted us to make a new attempt to study
	sentiment lexicon expansion. This paper first poses the problem as a PU
	learning problem, which is a new formulation. It then proposes a new PU
	learning method suitable for our problem using a neural network. The results
	are enhanced further with a new dictionary-based technique and a novel polarity
	classification technique. Experimental results show that the proposed approach
	outperforms baseline methods greatly.},
  url       = {https://www.aclweb.org/anthology/D17-1059}
}