@InProceedings{jimenezzafra-EtAl:2017:SemEval,
  author    = {Jim\'{e}nez-Zafra, Salud Mar\'{i}a  and  Montejo-R\'{a}ez, Arturo  and  Martin, Maite  and  Urena Lopez, L. Alfonso},
  title     = {SINAI at SemEval-2017 Task 4: User based 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     = {634--639},
  abstract  = {This document describes our participation in SemEval-2017 Task 4: Sentiment
	Analysis in Twitter. We have only reported results for subtask B - English,
	determining the polarity towards a topic on a two point scale (positive or
	negative sentiment). Our main contribution is the integration of user
	information in the classification process. A SVM model is trained with Word2Vec
	vectors from user's tweets extracted from his timeline. The obtained results
	show that user-specific classifiers trained on tweets from user timeline can
	introduce noise as they are error prone because they are classified by an
	imperfect system. This encourages us to explore further integration of user
	information for author-based Sentiment Analysis.},
  url       = {http://www.aclweb.org/anthology/S17-2104}
}