@InProceedings{schulder-EtAl:2017:I17-1,
  author    = {Schulder, Marc  and  Wiegand, Michael  and  Ruppenhofer, Josef  and  Roth, Benjamin},
  title     = {Towards Bootstrapping a Polarity Shifter Lexicon using Linguistic Features},
  booktitle = {Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 1: Long Papers)},
  month     = {November},
  year      = {2017},
  address   = {Taipei, Taiwan},
  publisher = {Asian Federation of Natural Language Processing},
  pages     = {624--633},
  abstract  = {We present a major step towards the creation of the first high-coverage lexicon
	of polarity shifters. In this work, we bootstrap a lexicon of verbs by
	exploiting various linguistic features. Polarity shifters, such as "abandon",
	are similar to negations (e.g. "not") in that they move the polarity of a
	phrase towards its inverse, as in "abandon all hope".
	While there exist lists of negation words, creating comprehensive lists of
	polarity shifters is far more challenging due to their sheer number. On a
	sample of manually annotated verbs we examine a variety of linguistic features
	for this task. Then we build a supervised classifier to increase coverage. 
	We show that this approach drastically reduces the annotation effort while
	ensuring a high-precision lexicon. We also show that our acquired knowledge of
	verbal polarity shifters improves phrase-level sentiment analysis.},
  url       = {http://www.aclweb.org/anthology/I17-1063}
}