@InProceedings{li-shah:2017:CoNLL,
  author    = {Li, Quanzhi  and  Shah, Sameena},
  title     = {Learning Stock Market Sentiment Lexicon and Sentiment-Oriented Word Vector from StockTwits},
  booktitle = {Proceedings of the 21st Conference on Computational Natural Language Learning (CoNLL 2017)},
  month     = {August},
  year      = {2017},
  address   = {Vancouver, Canada},
  publisher = {Association for Computational Linguistics},
  pages     = {301--310},
  abstract  = {Previous studies have shown that investor sentiment indicators can predict
	stock market change.  A domain-specific sentiment lexicon and
	sentiment-oriented word embedding model would help the sentiment analysis in
	financial domain and stock market. In this paper, we present a new approach to
	learning stock market lexicon from StockTwits, a popular financial social
	network for investors to share ideas.  It learns word polarity by predicting
	message sentiment, using a neural net-work.  The sentiment-oriented word
	embeddings are learned from tens of millions of StockTwits posts, and this is
	the first study presenting sentiment-oriented word embeddings for stock market.
	 The experiments of predicting investor sentiment show that our lexicon
	outperformed other lexicons built by the state-of-the-art methods, and the
	sentiment-oriented word vector was much better than the general word
	embeddings.},
  url       = {http://aclweb.org/anthology/K17-1031}
}