@InProceedings{yu-EtAl:2017:EMNLP20172,
  author    = {Yu, Liang-Chih  and  Wang, Jin  and  Lai, K. Robert  and  Zhang, Xuejie},
  title     = {Refining Word Embeddings for Sentiment Analysis},
  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     = {534--539},
  abstract  = {Word embeddings that can capture semantic and syntactic information from
	contexts have been extensively used for various natural language processing
	tasks. However, existing methods for learning context-based word embeddings
	typically fail to capture sufficient sentiment information. This may result in
	words with similar vector representations having an opposite sentiment polarity
	(e.g., good and bad), thus degrading sentiment analysis performance. Therefore,
	this study proposes a word vector refinement model that can be applied to any
	pre-trained word vectors (e.g., Word2vec and GloVe). The refinement model is
	based on adjusting the vector representations of words such that they can be
	closer to both semantically and sentimentally similar words and further away
	from sentimentally dissimilar words. Experimental results show that the
	proposed method can improve conventional word embeddings and outperform
	previously proposed sentiment embeddings for both binary and fine-grained
	classification on Stanford Sentiment Treebank (SST).},
  url       = {https://www.aclweb.org/anthology/D17-1056}
}