@InProceedings{maoquan-EtAl:2017:SemEval,
  author    = {maoquan, wang  and  Shiyun, Chen  and  yufei, Xie  and  lu, Zhao},
  title     = {EICA at SemEval-2017 Task 4: A Simple Convolutional Neural Network for Topic-based 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     = {737--740},
  abstract  = {This paper describes our approach for SemEval-2017 Task 4 - Sentiment Analysis
	in Twitter (SAT). Its five subtasks are divided into two categories: (1)
	sentiment classification, i.e., predicting topic-based tweet sentiment
	polarity, and (2) sentiment quantification, that is, estimating the sentiment
	distributions of a set of given tweets. We build a convolutional sentence
	classification system for the task of SAT. Official results show that the
	experimental results of our system are comparative.},
  url       = {http://www.aclweb.org/anthology/S17-2124}
}