@InProceedings{li-EtAl:2017:SemEval2,
  author    = {Li, Quanzhi  and  Shah, Sameena  and  Nourbakhsh, Armineh  and  Fang, Rui  and  Liu, Xiaomo},
  title     = {funSentiment at SemEval-2017 Task 5: Fine-Grained Sentiment Analysis on Financial Microblogs Using Word Vectors Built from StockTwits and Twitter},
  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     = {852--856},
  abstract  = {This paper describes the approach we used for SemEval-2017 Task 5: Fine-Grained
	Sentiment Analysis on Financial Microblogs. We use three types of word
	embeddings in our algorithm: word embeddings learned from 200 million tweets,
	sentiment-specific word embeddings learned from 10 million tweets using
	distance supervision, and word embeddings learned from 20 million StockTwits
	messages.  In our approach, we also take the left and right context of the
	target company into consideration when generating polarity prediction features.
	All the features generated from different word embeddings and contexts are
	integrated together to train our algorithm},
  url       = {http://www.aclweb.org/anthology/S17-2145}
}