Moore, Andrew and Rayson, Paul Edward (2017) Lancaster A at SemEval-2017 Task 5: Evaluation metrics matter : predicting sentiment from financial news headlines. In: Proceedings of the 11th International Workshop on Semantic Evaluations (SemEval-2017) :. Association for Computational Linguistics, Stroudsburg, PA, pp. 581-585. ISBN 9781945626555
lancaster_semeval_2017.pdf - Accepted Version
Available under License Creative Commons Attribution.
Download (159kB)
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.