@InProceedings{jiang-lan-wu:2017:SemEval,
  author    = {Jiang, Mengxiao  and  Lan, Man  and  Wu, Yuanbin},
  title     = {ECNU at SemEval-2017 Task 5: An Ensemble of Regression Algorithms with Effective Features for Fine-Grained Sentiment Analysis in Financial Domain},
  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     = {888--893},
  abstract  = {This paper describes our systems submitted to the Fine-Grained Sentiment
	Analysis on Financial Microblogs and News task (i.e., Task 5) in SemEval-2017.
	This task includes two subtasks in microblogs and news headline domain
	respectively. To settle this problem, we extract four types of effective
	features, including linguistic features, sentiment lexicon features,
	domain-specific features and word embedding features. Then we employ these
	features to construct models by using ensemble regression algorithms. Our
	submissions rank 1st and rank 5th in subtask 1 and subtask 2 respectively.},
  url       = {http://www.aclweb.org/anthology/S17-2152}
}