@InProceedings{tamilselvam-EtAl:2017:I17-1,
  author    = {Tamilselvam, Srikanth  and  Nagar, Seema  and  Mishra, Abhijit  and  Dey, Kuntal},
  title     = {Graph Based Sentiment Aggregation using ConceptNet Ontology},
  booktitle = {Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 1: Long Papers)},
  month     = {November},
  year      = {2017},
  address   = {Taipei, Taiwan},
  publisher = {Asian Federation of Natural Language Processing},
  pages     = {525--535},
  abstract  = {The sentiment aggregation problem accounts for analyzing the sentiment of a
	user towards various aspects/features of a product, and meaningfully
	assimilating the pragmatic significance of these features/aspects from an
	opinionated text. The current paper addresses the sentiment aggregation
	problem, by assigning weights to each aspect appearing in the user-generated
	content, that are proportionate to the strategic importance of the aspect in
	the pragmatic domain. The novelty of this paper is in computing the pragmatic
	significance (weight) of each aspect, using graph centrality measures (applied
	on domain specific ontology-graphs extracted from ConceptNet), and deeply
	ingraining these weights while aggregating the sentiments from opinionated
	text. We experiment over multiple real-life product review data. Our system
	consistently outperforms the state of the art - by as much as a F-score of
	20.39% in one case.},
  url       = {http://www.aclweb.org/anthology/I17-1053}
}