@InProceedings{kanishcheva-bobicev:2017:RANLP,
  author    = {Kanishcheva, Olga  and  Bobicev, Victoria},
  title     = {Good News vs. Bad News: What are they talking about?},
  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     = {325--333},
  abstract  = {Today’s massive news streams demand the automate analysis which is provided
	by various online news explorers. However, most of them do not provide
	sentiment analysis. The main problem of sentiment analysis of news is the
	differences between the writers and readers attitudes to the news text. News
	can be good or bad but have to be delivered in neutral words as pure facts.
	Although there are applications for sentiment analysis of news, the task of
	news analysis is still a very actual problem because the latest news impacts
	people’s lives daily.
	In this paper, we explored the problem of sentiment analysis for Ukrainian and
	Russian news, developed a corpus of Ukrainian and Russian news and annotated
	each text using one of three categories: positive, negative and neutral. Each
	text was marked by at least three independent annotators via the web interface,
	the inter-annotator agreement was analyzed and the final label for each text
	was computed. These texts were used in the machine learning experiments.
	Further, we investigated what kinds of named entities such as Locations,
	Organizations, Persons are perceived as good or bad by the readers and which of
	them were the cause for text annotation ambiguity.},
  url       = {https://doi.org/10.26615/978-954-452-049-6_044}
}