@InProceedings{ibeke-EtAl:2017:I17-2,
  author    = {Ibeke, Ebuka  and  Lin, Chenghua  and  Wyner, Adam  and  Barawi, Mohamad Hardyman},
  title     = {Extracting and Understanding Contrastive Opinion through Topic Relevant Sentences},
  booktitle = {Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 2: Short Papers)},
  month     = {November},
  year      = {2017},
  address   = {Taipei, Taiwan},
  publisher = {Asian Federation of Natural Language Processing},
  pages     = {395--400},
  abstract  = {Contrastive opinion mining is essential in identifying, extracting and
	organising opinions from user generated texts. Most existing studies separate
	input data into respective collections. In addition, the relationships between
	the topics  extracted and the sentences in the corpus which express the topics
	are opaque, hindering our understanding of the opinions expressed in the
	corpus. We propose a novel unified latent variable model (contraLDA) which
	addresses the above matters. Experimental results show the effectiveness of our
	model in mining contrasted opinions, outperforming our baselines.},
  url       = {http://www.aclweb.org/anthology/I17-2067}
}