@InProceedings{cabanski-romberg-conrad:2017:SemEval,
  author    = {Cabanski, Tobias  and  Romberg, Julia  and  Conrad, Stefan},
  title     = {HHU at SemEval-2017 Task 5: Fine-Grained Sentiment Analysis on Financial Data using Machine Learning Methods},
  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     = {832--836},
  abstract  = {In this Paper a system for solving SemEval-2017 Task 5 is presented. This task
	is divided into two tracks where the sentiment of microblog messages and news
	headlines has to be predicted. Since two submissions were allowed, two
	different machine learning methods were developed to solve this task, a support
	vector machine approach and a recurrent neural network approach. To feed in
	data for these approaches, different feature extraction methods are used,
	mainly word representations and lexica. The best submissions for both tracks
	are provided by the recurrent neural network which achieves a F1-score of 0.729
	in track 1 and 0.702 in track 2.},
  url       = {http://www.aclweb.org/anthology/S17-2141}
}