@InProceedings{pateria:2016:COLING,
  author    = {Pateria, Shubham},
  title     = {Aspect Based Sentiment Analysis using Sentiment Flow with Local and Non-local Neighbor Information},
  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     = {2635--2646},
  abstract  = {Aspect-level analysis of sentiments contained in a review text is important to
	reveal a detailed picture of consumer opinions. While a plethora of methods
	have been traditionally employed for this task, majority focus has been on
	analyzing only aspect-centered local information. However, incorporating
	context information from non-local aspect neighbors may capture richer
	structure in review text and enhance prediction. This may especially be helpful
	to resolve ambiguous predictions. The context around an aspect can be
	incorporated using semantic relations within text and inter-label dependencies
	in the output. On the output side, this becomes a structured prediction task.
	However, non-local label correlations are computationally heavy and intractable
	to infer for structured prediction models like Conditional Random Fields (CRF).
	Moreover, some prior intuition is required to incorporate non-local context.
	Thus, inspired by previous research on multi-stage prediction, we propose a
	two-level model for aspect-based analysis. The proposed model uses predicted
	probability estimates from first level to incorporate neighbor information in
	the second level. The model is evaluated on data taken from SemEval Workshops
	and Bing Liu's review collection. It shows comparatively better performance
	against few existing methods. Overall, we get prediction accuracy in a range of
	83-88\% and almost 3-4 point increment against baseline (first level only)
	scores.},
  url       = {http://aclweb.org/anthology/C16-1248}
}