@InProceedings{abreu-EtAl:2017:SemEval,
  author    = {Abreu, Jos\'{e}  and  Castro, Iv\'{a}n  and  Mart\'{i}nez, Claudia  and  Oliva, Sebasti\'{a}n  and  Guti\'{e}rrez, Yoan},
  title     = {UCSC-NLP at SemEval-2017 Task 4: Sense n-grams for Sentiment Analysis in Twitter},
  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     = {807--811},
  abstract  = {This paper describes the system submitted to SemEval-2017 Task 4-A Sentiment
	Analysis in Twitter developed by the UCSC-NLP team. We studied how
	relationships between sense n-grams and sentiment polarities can contribute to
	this task, i.e. co-occurrences of WordNet senses in the tweet, and the
	polarity. Furthermore, we evaluated the effect of discarding a large set of
	features based on char-grams reported in preceding works. Based on these
	elements, we developed a SVM system, which exploring SentiWordNet as a polarity
	lexicon. It achieves an $F\_1=0.624$ of average. Among $39$ submissions to this
	task, we ranked $10th$.},
  url       = {http://www.aclweb.org/anthology/S17-2136}
}