@InProceedings{dibuono-EtAl:2017:NLPmJ,
  author    = {di Buono, Maria Pia  and  \v{S}najder, Jan  and  Dalbelo Basic, Bojana  and  Glava\v{s}, Goran  and  Tutek, Martin  and  Milic-Frayling, Natasa},
  title     = {Predicting News Values from Headline Text and Emotions},
  booktitle = {Proceedings of the 2017 EMNLP Workshop: Natural Language Processing meets Journalism},
  month     = {September},
  year      = {2017},
  address   = {Copenhagen, Denmark},
  publisher = {Association for Computational Linguistics},
  pages     = {1--6},
  abstract  = {We present a preliminary study on predicting news values from headline text and
	emotions. We perform a multivariate analysis on a dataset manually annotated
	with news values and emotions, discovering interesting correlations among them.
	We then train two competitive machine learning models -- an SVM and a CNN --
	to predict news values from headline text and emotions as features. We find
	that, while both models yield a satisfactory performance, some news values are
	more difficult to detect than others, while some profit more from including
	emotion information.},
  url       = {http://www.aclweb.org/anthology/W17-4201}
}