@InProceedings{kaljahi-foster:2016:PEOPLES,
  author    = {Kaljahi, Rasoul  and  Foster, Jennifer},
  title     = {Detecting Opinion Polarities using Kernel Methods},
  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     = {60--69},
  abstract  = {We investigate the application of kernel methods to representing both
	structural and lexical knowledge for predicting polarity of opinions in
	consumer product review.  We introduce any-gram kernels which model lexical
	information in a significantly faster way than the traditional n-gram features,
	while capturing all possible orders of n-grams n in a sequence without the need
	to explicitly present a pre-specified set of such orders. We also present an
	effective format to represent constituency and dependency structure together
	with aspect terms and sentiment polarity scores. Furthermore, we modify the
	traditional tree kernel function to compute the similarity based on word
	embedding vectors instead of exact string match and present experiments using
	the new models.},
  url       = {http://aclweb.org/anthology/W16-4307}
}