@InProceedings{cortis-EtAl:2017:SemEval,
  author    = {Cortis, Keith  and  Freitas, Andr\'{e}  and  Daudert, Tobias  and  Huerlimann, Manuela  and  Zarrouk, Manel  and  Handschuh, Siegfried  and  Davis, Brian},
  title     = {SemEval-2017 Task 5: Fine-Grained Sentiment Analysis on Financial Microblogs and News},
  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     = {519--535},
  abstract  = {This paper discusses the "Fine-Grained Sentiment Analysis on Financial
	Microblogs and News" task as part of SemEval-2017, specifically under the
	"Detecting sentiment, humour, and truth" theme. This task contains two tracks,
	where the first one concerns Microblog messages and the second one covers News
	Statements and Headlines. The main goal behind both tracks was to predict the
	sentiment score for each of the mentioned companies/stocks. The sentiment
	scores for each text instance adopted floating point values in the range of -1
	(very negative/bearish) to 1 (very positive/bullish), with 0 designating
	neutral sentiment. This task attracted a total of 32 participants, with 25
	participating in Track 1 and 29 in Track 2.},
  url       = {http://www.aclweb.org/anthology/S17-2089}
}