@InProceedings{zini-becker-dias:2017:SemEval,
  author    = {Zini, Tiago  and  Becker, Karin  and  Dias, Marcelo},
  title     = {INF-UFRGS at SemEval-2017 Task 5: A Supervised Identification of Sentiment Score in Tweets and 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     = {837--841},
  abstract  = {This paper describes a supervised solution for detecting the polarity scores of
	tweets or headline news in the financial domain, submitted to the SemEval 2017
	Fine-Grained Sentiment Analysis on Financial Microblogs and News Task. The
	premise is that it is possible to understand market reaction over a company
	stock by measuring the positive/negative sentiment contained in the financial
	tweets and news headlines, where polarity is measured in a continuous scale
	ranging from -1.0 (very bearish) to 1.0 (very bullish). Our system receives as
	input the textual content of tweets or news headlines, together with their ids,
	stock cashtag or name of target company, and the polarity score gold standard
	for the training dataset. Our solution retrieves features from these text
	instances using n-gram, hashtags, sentiment score calculated by a external APIs
	and others features to train a regression model capable to detect continuous
	score of these sentiments with precision.},
  url       = {http://www.aclweb.org/anthology/S17-2142}
}