@InProceedings{sharma-EtAl:2017:EMNLP2017,
  author    = {Sharma, Raksha  and  Somani, Arpan  and  Kumar, Lakshya  and  Bhattacharyya, Pushpak},
  title     = {Sentiment Intensity Ranking among Adjectives Using Sentiment Bearing Word Embeddings},
  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     = {547--552},
  abstract  = {Identification of intensity ordering among polar (positive or negative) words
	which have the same semantics can lead to a fine-grained sentiment analysis.
	For
	example, 'master', 'seasoned' and 'familiar' point to different intensity
	levels, though they all convey the same meaning (semantics), i.e., expertise:
	having a good
	knowledge of. In this paper, we propose a semi-supervised technique that uses
	sentiment
	bearing word embeddings to produce a continuous ranking among adjectives that
	share common semantics. Our system demonstrates a strong Spearman’s rank
	correlation of 0.83 with the gold standard ranking. We show that sentiment
	bearing word embeddings facilitate a more accurate intensity ranking system
	than other standard word embeddings (word2vec and GloVe). Word2vec is the
	state-of-the-art for intensity ordering task.},
  url       = {https://www.aclweb.org/anthology/D17-1058}
}