@InProceedings{wang-xia:2017:EMNLP2017,
  author    = {Wang, Leyi  and  Xia, Rui},
  title     = {Sentiment Lexicon Construction with Representation Learning Based on Hierarchical Sentiment Supervision},
  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     = {502--510},
  abstract  = {Sentiment lexicon is an important tool for identifying the sentiment polarity
	of words and texts. How to automatically construct sentiment lexicons has
	become a research topic in the field of sentiment analysis and opinion mining.
	Recently there were some attempts to employ representation learning algorithms
	to construct a sentiment lexicon with sentiment-aware word embedding. However,
	these methods were normally trained under document-level sentiment supervision.
	In this paper, we develop a neural architecture to train a sentiment-aware word
	embedding by integrating the sentiment supervision at both  document and word
	levels, to enhance the quality of word embedding as well as the sentiment
	lexicon. Experiments on the SemEval 2013-2016 datasets indicate that the
	sentiment lexicon generated by our approach achieves the state-of-the-art
	performance in both supervised and unsupervised sentiment classification, in
	comparison with several strong sentiment lexicon construction methods.},
  url       = {https://www.aclweb.org/anthology/D17-1052}
}