@InProceedings{skeppstedt-EtAl:2016:PEOPLES,
  author    = {Skeppstedt, Maria  and  Sahlgren, Magnus  and  Paradis, Carita  and  Kerren, Andreas},
  title     = {Active learning for detection of stance components},
  booktitle = {Proceedings of the Workshop on Computational Modeling of People's Opinions, Personality, and Emotions in Social Media (PEOPLES)},
  month     = {December},
  year      = {2016},
  address   = {Osaka, Japan},
  publisher = {The COLING 2016 Organizing Committee},
  pages     = {50--59},
  abstract  = {Automatic detection of five language components, which are all relevant for
	expressing opinions and for stance taking, was studied: positive sentiment,
	negative sentiment, speculation, contrast and condition. A resource-aware
	approach was taken, which included manual annotation of 500 training samples
	and the use of limited lexical resources. Active learning was compared to
	random selection of training data, as well as to a lexicon-based method. Active
	learning was successful for the categories speculation, contrast and condition,
	but not for the two sentiment categories, for which results achieved when using
	active learning were similar to those achieved when applying a random selection
	of training data. This difference is likely due to a larger variation in how
	sentiment is expressed than in how speakers express the other three categories.
	This larger variation was also shown by the lower recall results achieved by
	the lexicon-based approach for sentiment than for the categories speculation,
	contrast and condition.},
  url       = {http://aclweb.org/anthology/W16-4306}
}