@InProceedings{jovanoski-pachovski-nakov:2016:COLING,
  author    = {Jovanoski, Dame  and  Pachovski, Veno  and  Nakov, Preslav},
  title     = {On the Impact of Seed Words on Sentiment Polarity Lexicon Induction},
  booktitle = {Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers},
  month     = {December},
  year      = {2016},
  address   = {Osaka, Japan},
  publisher = {The COLING 2016 Organizing Committee},
  pages     = {1557--1567},
  abstract  = {Sentiment polarity lexicons are key resources for sentiment analysis, and
	researchers have invested a lot of efforts in their manual creation. However,
	there has been a recent shift towards automatically extracted lexicons, which
	are orders of magnitude larger and perform much better. These lexicons are
	typically mined using bootstrapping, starting from very few seed words
	whose polarity is given,
	e.g., 50-60 words, and sometimes even just 5-6. 
	Here we demonstrate that much higher-quality lexicons can be built by starting
	with hundreds of words and phrases as seeds, especially when they are
	in-domain. Thus, we combine (i) mid-sized high-quality manually crafted
	lexicons as seeds and (ii) bootstrapping, in order to build large-scale
	lexicons.},
  url       = {http://aclweb.org/anthology/C16-1147}
}