@InProceedings{symeonidis-EtAl:2017:SemEval2,
  author    = {Symeonidis, Symeon  and  Kordonis, John  and  Effrosynidis, Dimitrios  and  Arampatzis, Avi},
  title     = {DUTH at SemEval-2017 Task 5: Sentiment Predictability in Financial Microblogging and News Articles},
  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     = {861--865},
  abstract  = {We present the system developed by the team DUTH for the participation in
	Semeval-2017 task 5 - Fine-Grained Sentiment Analysis on Financial Microblogs
	and News, in subtasks A and B. Our approach to determine the sentiment of
	Microblog Messages and News Statements \& Headlines is based on linguistic
	preprocessing, feature engineering, and supervised machine learning techniques.
	To train our model, we used Neural Network Regression, Linear Regression,
	Boosted Decision Tree Regression and Decision Forrest Regression classifiers to
	forecast sentiment scores. At the end, we present an error measure, so as to
	improve the performance about forecasting methods of the
	system.},
  url       = {http://www.aclweb.org/anthology/S17-2147}
}