@InProceedings{moore-rayson:2017:SemEval,
  author    = {Moore, Andrew  and  Rayson, Paul},
  title     = {Lancaster A at SemEval-2017 Task 5: Evaluation metrics matter: predicting sentiment from financial news headlines},
  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     = {581--585},
  abstract  = {This paper describes our participation in Task 5 track 2 of SemEval 2017 to
	predict the sentiment of financial news headlines for a specific company on a
	continuous scale between -1 and 1. We tackled the problem using a number of
	approaches, utilising a Support Vector Regression (SVR) and a Bidirectional
	Long Short-Term Memory (BLSTM). We found an improvement of 4-6\% using the LSTM
	model over the SVR and came fourth in the track. We report a number of
	different evaluations using a finance specific word embedding model and reflect
	on the effects of using different evaluation metrics.},
  url       = {http://www.aclweb.org/anthology/S17-2095}
}