@InProceedings{bakken-EtAl:2016:COLING,
  author    = {Bakken, Patrik F.  and  Bratlie, Terje A.  and  Marco, Cristina  and  Gulla, Jon Atle},
  title     = {Political News Sentiment Analysis for Under-resourced Languages},
  booktitle = {Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers},
  month     = {December},
  year      = {2016},
  address   = {Osaka, Japan},
  publisher = {The COLING 2016 Organizing Committee},
  pages     = {2989--2996},
  abstract  = {This paper presents classification results for the analysis of sentiment in
	political news articles. The domain of political news is particularly
	challenging, as journalists are presumably objective, whilst at the same time
	opinions can be subtly expressed. To deal with this challenge, in this work we
	conduct a two-step classification model, distinguishing first subjective and
	second positive and negative sentiment texts. More specifically, we propose a
	shallow machine learning approach where only minimal features are needed to
	train the classifier, including sentiment-bearing Co-Occurring Terms (COTs)
	and negation words. This approach yields close to state-of-the-art results.
	Contrary to results in other domains, the use of negations as features does not
	have a positive impact in the evaluation results. This method is particularly
	suited for languages that suffer from a lack of resources, such as sentiment
	lexicons or parsers, and for those systems that need to function in real-time.},
  url       = {http://aclweb.org/anthology/C16-1281}
}