@InProceedings{wang-EtAl:2017:EACLlong,
  author    = {Wang, Bo  and  Liakata, Maria  and  Zubiaga, Arkaitz  and  Procter, Rob},
  title     = {TDParse: Multi-target-specific sentiment recognition on Twitter},
  booktitle = {Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 1, Long Papers},
  month     = {April},
  year      = {2017},
  address   = {Valencia, Spain},
  publisher = {Association for Computational Linguistics},
  pages     = {483--493},
  abstract  = {Existing target-specific sentiment recognition methods consider only a single
	target per tweet, and have been shown to miss nearly half of the actual targets
	mentioned. We present a corpus of UK election tweets, with an average of 3.09
	entities per tweet and more than one type of sentiment in half of the tweets.
	This requires a method for multi-target specific sentiment recognition, which
	we develop by using the context around a target as well as syntactic
	dependencies involving the target. We present results of our method on both a
	benchmark corpus of single targets and the multi-target election corpus,
	showing state-of-the art performance in both corpora and outperforming previous
	approaches to multi-target sentiment task as well as deep learning models for
	single-target sentiment.},
  url       = {http://www.aclweb.org/anthology/E17-1046}
}