Lancaster A at SemEval-2017 Task 5: Evaluation metrics matter: predicting sentiment from financial news headlines
This work addresses sentiment analysis for financial news, but it is incremental as it applies existing methods to a specific competition task.
The paper tackled sentiment prediction of financial news headlines on a continuous scale, finding that a Bidirectional LSTM model improved performance by 4-6% over an SVR approach, placing fourth in the SemEval 2017 task.
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.