@InProceedings{mirandajimenez-EtAl:2017:SemEval,
  author    = {Miranda-Jim\'{e}nez, Sabino  and  Graff, Mario  and  Tellez, Eric Sadit  and  Moctezuma, Daniela},
  title     = {INGEOTEC at SemEval 2017 Task 4: A B4MSA Ensemble based on Genetic Programming for Twitter Sentiment Analysis},
  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     = {771--776},
  abstract  = {This paper describes the system used in SemEval-2017 Task 4 (Subtask A):
	Message Polarity Classification for both English and Arabic languages. Our
	proposed system is an ensemble of two layers, the first one uses our generic
	framework for multilingual polarity classification (B4MSA) and the second layer
	combines all the decision function values predicted by B4MSA systems using a
	non-linear function evolved using a Genetic Programming system, EvoDAG. With
	this approach, the best performances reached by our system were macro-recall
	0.68 (English) and 0.477 (Arabic) which set us in sixth and fourth positions in
	the results table, respectively.},
  url       = {http://www.aclweb.org/anthology/S17-2130}
}