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

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

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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.

Item Type:
Contribution in Book/Report/Proceedings
ID Code:
85958
Deposited By:
Deposited On:
20 Apr 2017 10:52
Refereed?:
Yes
Published?:
Published
Last Modified:
27 Sep 2020 06:36