@InProceedings{troncy-EtAl:2017:SemEval,
  author    = {Troncy, Raphael  and  Palumbo, Enrico  and  Sygkounas, Efstratios  and  Rizzo, Giuseppe},
  title     = {SentiME++ at SemEval-2017 Task 4: Stacking State-of-the-Art Classifiers to Enhance Sentiment Classification},
  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     = {648--652},
  abstract  = {In this paper, we describe the participation of the SentiME++ system to the
	SemEval 2017 Task 4A "Sentiment Analysis in Twitter" that aims to classify
	whether English tweets are of positive, neutral or negative sentiment.
	SentiME++ is an ensemble approach to sentiment analysis that leverages stacked
	generalization to automatically combine the predictions of five
	state-of-the-art sentiment classifiers. SentiME++ achieved officially 61.30%
	F1-score, ranking 12th out of 38 participants.},
  url       = {http://www.aclweb.org/anthology/S17-2107}
}