@InProceedings{rotim-tutek-vsnajder:2017:SemEval,
  author    = {Rotim, Leon  and  Tutek, Martin  and  \v{S}najder, Jan},
  title     = {TakeLab at SemEval-2017 Task 5: Linear aggregation of word embeddings for fine-grained sentiment analysis of financial news},
  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     = {866--871},
  abstract  = {This paper describes our system for fine-grained sentiment scoring of news
	headlines submitted to SemEval 2017 task 5--subtask 2. Our system uses a
	feature-light method that consists of a Support Vector Regression (SVR) with
	various kernels and word vectors as features. Our best-performing submission
	scored 3rd on the task out of 29 teams and 4th out of 45 submissions with a
	cosine score of 0.733.},
  url       = {http://www.aclweb.org/anthology/S17-2148}
}