@InProceedings{mullick-goyal-ganguly:2016:PEOPLES,
  author    = {Mullick, Ankan  and  Goyal, Pawan  and  Ganguly, Niloy},
  title     = {A graphical framework to detect and categorize diverse opinions from online news},
  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     = {40--49},
  abstract  = {This paper proposes a graphical framework to extract  opinionated sentences
	which highlight different contexts within a given news article by introducing
	the concept of diversity in a graphical model for opinion detection.We conduct
	extensive evaluations and find that the proposed modification leads to
	impressive improvement in performance and makes the final results of the model
	much more usable. The proposed method (OP-D) not only performs much better than
	the other techniques used for opinion detection as well as introducing
	diversity, but is also able to select opinions from different categories {Asher
	et al. 2009 Appraisal}. By developing a classification model which categorizes
	the identified sentences into various opinion categories, we find that OP-D is
	able to push opinions from different categories uniformly among the top
	opinions.},
  url       = {http://aclweb.org/anthology/W16-4305}
}