@InProceedings{poddar-hsu-lee:2017:EMNLP2017,
  author    = {Poddar, Lahari  and  Hsu, Wynne  and  Lee, Mong Li},
  title     = {Author-aware Aspect Topic Sentiment Model to Retrieve Supporting Opinions from Reviews},
  booktitle = {Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing},
  month     = {September},
  year      = {2017},
  address   = {Copenhagen, Denmark},
  publisher = {Association for Computational Linguistics},
  pages     = {472--481},
  abstract  = {User generated content about products and services in the form of reviews are
	often diverse and even contradictory. This makes it difficult for users to know
	if an opinion in a review is prevalent or biased.
	We study the problem of searching for supporting opinions in the context of
	reviews. We propose a framework called SURF, that first identifies opinions
	expressed in a review, and then finds similar opinions from other reviews. We
	design a novel probabilistic graphical model that captures opinions as a
	combination of aspect, topic and sentiment dimensions, takes into account the
	preferences of individual authors, as well as the quality of the  entity under
	review, and encodes the flow of thoughts in a review by constraining the aspect
	distribution dynamically among successive review segments. We derive a
	similarity measure that  considers both lexical and semantic similarity to find
	supporting opinions. Experiments on TripAdvisor hotel reviews and Yelp
	restaurant reviews  show that  our model outperforms  existing methods for
	modeling opinions, and the proposed framework is effective in finding
	supporting opinions.},
  url       = {https://www.aclweb.org/anthology/D17-1049}
}