@InProceedings{pivovarova-EtAl:2017:SemEval,
  author    = {Pivovarova, Lidia  and  Escoter, Lloren\c{c}  and  Klami, Arto  and  Yangarber, Roman},
  title     = {HCS at SemEval-2017 Task 5: Polarity detection in business news using convolutional neural networks},
  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     = {842--846},
  abstract  = {Task 5 of SemEval-2017 involves fine-grained sentiment analysis on financial
	microblogs and news.  Our solution for determining the sentiment score extends
	an earlier convolutional neural network for sentiment analysis in several ways.
	 We explicitly encode a focus on a particular company, we apply a data
	augmentation   scheme, and use a larger data collection to complement the small
	training data provided by the task organizers.                          The best
	results
	were
	achieved
	by training a model on an external dataset and then tuning it using the
	provided training dataset.},
  url       = {http://www.aclweb.org/anthology/S17-2143}
}