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Lancaster A at SemEval-2017 Task 5: Evaluation metrics matter: predicting sentiment from financial news headlines

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Publication date4/08/2017
Host publicationProceedings of the 11th International Workshop on Semantic Evaluations (SemEval-2017)
Place of PublicationStroudsburg, PA
PublisherAssociation for Computational Linguistics
Number of pages5
ISBN (Print)9781945626555
<mark>Original language</mark>English


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.