@InProceedings{mansar-EtAl:2017:SemEval,
  author    = {Mansar, Youness  and  Gatti, Lorenzo  and  Ferradans, Sira  and  Guerini, Marco  and  Staiano, Jacopo},
  title     = {Fortia-FBK at SemEval-2017 Task 5: Bullish or Bearish? Inferring Sentiment towards Brands from Financial News 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     = {817--822},
  abstract  = {In this paper, we describe a methodology to infer Bullish or Bearish sentiment
	towards companies/brands. More specifically, our approach leverages affective
	lexica and word embeddings in combination with convolutional neural networks to
	infer the sentiment of financial news headlines towards a target company. Such
	architecture was used and evaluated in the context of the SemEval 2017
	challenge (task 5, subtask 2), in which it obtained the best performance.},
  url       = {http://www.aclweb.org/anthology/S17-2138}
}