@InProceedings{saleiro-EtAl:2017:SemEval,
  author    = {Saleiro, Pedro  and  Mendes Rodrigues, Eduarda  and  Soares, Carlos  and  Oliveira, Eug\'{e}nio},
  title     = {FEUP at SemEval-2017 Task 5: Predicting Sentiment Polarity and Intensity with Financial Word Embeddings},
  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     = {904--908},
  abstract  = {This paper presents the approach developed at the Faculty of Engineering of
	University of Porto, to participate in SemEval 2017, Task 5: Fine-grained
	Sentiment Analysis on Financial Microblogs and News. 
	The task consisted in predicting a real continuous variable from -1.0 to +1.0
	representing the polarity and intensity of sentiment concerning
	companies/stocks mentioned in short texts. We modeled the task as a regression
	analysis problem and combined traditional techniques such as pre-processing
	short texts, bag-of-words representations and lexical-based features with
	enhanced financial specific bag-of-embeddings. We used an external collection
	of tweets and news headlines mentioning companies/stocks from S\&P 500 to
	create financial word embeddings which are able to capture domain-specific
	syntactic and semantic similarities. The resulting approach obtained a cosine
	similarity score of 0.69 in sub-task 5.1 - Microblogs and 0.68 in sub-task 5.2
	- News Headlines.},
  url       = {http://www.aclweb.org/anthology/S17-2155}
}