@InProceedings{schouten-frasincar-dejong:2017:SemEval,
  author    = {Schouten, Kim  and  Frasincar, Flavius  and  de Jong, Franciska},
  title     = {COMMIT at SemEval-2017 Task 5: Ontology-based Method for Sentiment Analysis of Financial Headlines},
  booktitle = {Proceedings of the 11th International Workshop on Semantic Evaluation (SemEval-2017)},
  month     = {August},
  year      = {2017},
  address   = {Vancouver, Canada},
  publisher = {Association for Computational Linguistics},
  pages     = {883--887},
  abstract  = {This paper describes our submission to Task 5 of SemEval 2017, Fine-Grained
	Sentiment Analysis on Financial Microblogs and News, where we limit ourselves
	to performing sentiment analysis on news headlines only (track 2). The approach
	presented in this paper uses a Support Vector Machine to do the required
	regression, and besides unigrams and a sentiment tool, we use various
	ontology-based features. To this end we created a domain ontology that models
	various concepts from the financial domain. This allows us to model the
	sentiment of actions depending on which entity they are affecting (e.g.,
	'decreasing debt' is positive, but 'decreasing profit' is negative). The
	presented approach yielded a cosine distance of 0.6810 on the official test
	data, resulting in the 12th position.},
  url       = {http://www.aclweb.org/anthology/S17-2151}
}