@InProceedings{gimenezperez-francosalvador-rosso:2017:EACLshort,
  author    = {Gim\'{e}nez-P\'{e}rez, Rosa M.  and  Franco-Salvador, Marc  and  Rosso, Paolo},
  title     = {Single and Cross-domain Polarity Classification using String Kernels},
  booktitle = {Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 2, Short Papers},
  month     = {April},
  year      = {2017},
  address   = {Valencia, Spain},
  publisher = {Association for Computational Linguistics},
  pages     = {558--563},
  abstract  = {The polarity classification task aims at automatically identifying  whether a
	subjective text is positive or negative. When the target domain is different
	from those where a model was trained, we refer to a cross-domain setting. That
	setting usually implies the use of a domain adaptation method. In this work, we
	study the single and cross-domain polarity classification tasks from the string
	kernels perspective. Contrary to classical domain adaptation methods, which
	employ texts from both domains to detect pivot features, we do not use the
	target domain for training. Our approach detects the lexical peculiarities that
	characterise the text polarity and maps them into a domain independent space by
	means of kernel discriminant analysis. Experimental results show
	state-of-the-art performance in single and cross-domain polarity
	classification.},
  url       = {http://www.aclweb.org/anthology/E17-2089}
}