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.
@article{arxiv.1705.00571,
title = {Lancaster A at SemEval-2017 Task 5: Evaluation metrics matter: predicting sentiment from financial news headlines},
author = {Andrew Moore and Paul Rayson},
journal= {arXiv preprint arXiv:1705.00571},
year = {2018}
}
Comments
5 pages, to Appear in the Proceedings of the 11th International Workshop on Semantic Evaluation (SemEval 2017), August 2017, Vancouver, BC