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

Andrew Moore, Paul Rayson


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.
Anthology ID:
S17-2095
Volume:
Proceedings of the 11th International Workshop on Semantic Evaluation (SemEval-2017)
Month:
August
Year:
2017
Address:
Vancouver, Canada
Venue:
*SEMEVAL
SIGs:
SIGLEX | SIGSEM
Publisher:
Association for Computational Linguistics
Note:
Pages:
581–585
Language:
URL:
https://www.aclweb.org/anthology/S17-2095
DOI:
10.18653/v1/S17-2095
Bib Export formats:
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PDF:
http://aclanthology.lst.uni-saarland.de/S17-2095.pdf
Poster:
 S17-2095.Poster.pdf
Presentation:
 S17-2095.Presentation.pdf