@InProceedings{tabari-seyeditabari-zadrozny:2017:SemEval,
  author    = {Tabari, Narges  and  Seyeditabari, Armin  and  Zadrozny, Wlodek},
  title     = {SentiHeros at SemEval-2017 Task 5: An application of Sentiment Analysis on Financial Tweets},
  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     = {857--860},
  abstract  = {Sentiment analysis is the process of identifying the opinion expressed in text.
	Recently it has been used to study behavioral finance, and in particular the
	effect of opinions and emotions on economic or financial decisions.
	SemEval-2017 task 5 focuses on the financial market as the do- main for
	sentiment analysis of text; specifically, task 5, subtask 1 focuses on
	financial tweets about stock symbols. In this paper, we describe a machine
	learning classifier for binary classification of financial tweets. We used
	natural language processing techniques and the random forest algorithm to train
	our model, and tuned it for the training dataset of Task 5, subtask 1. Our
	system achieves the 7th rank on the leaderboard of the task.},
  url       = {http://www.aclweb.org/anthology/S17-2146}
}