@InProceedings{steinberger-EtAl:2017:RANLP2,
  author    = {Steinberger, Ralf  and  Hegele, Stefanie  and  Tanev, Hristo  and  della Rocca, Leonida},
  title     = {Large-scale news entity sentiment analysis},
  booktitle = {Proceedings of the International Conference Recent Advances in Natural Language Processing, RANLP 2017},
  month     = {September},
  year      = {2017},
  address   = {Varna, Bulgaria},
  publisher = {INCOMA Ltd.},
  pages     = {707--715},
  abstract  = {We work on detecting positive or negative sentiment towards named entities in
	very large volumes of news articles. The aim is to monitor changes over time,
	as well as to work towards media bias detection by com-paring differences
	across news sources and countries. With view to applying the same method to
	dozens of languages, we use lin-guistically light-weight methods: searching for
	positive and negative terms in bags of words around entity mentions (also
	consid-ering negation). Evaluation results are good and better than a
	third-party baseline sys-tem, but precision is not sufficiently high to display
	the results publicly in our multilin-gual news analysis system Europe Media
	Monitor (EMM). In this paper, we focus on describing our effort to improve the
	English language results by avoiding the biggest sources of errors. We also
	present new work on using a syntactic parser to identify safe opinion
	recognition rules, such as predica-tive structures in which sentiment words
	di-rectly refer to an entity. The precision of this method is good, but recall
	is very low.},
  url       = {https://doi.org/10.26615/978-954-452-049-6_091}
}