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

## TL;DR

This paper explores predicting financial news headline sentiment on a continuous scale using SVR and BLSTM models, highlighting the importance of evaluation metrics and achieving competitive results.

## Contribution

It introduces the application of LSTM and SVR models for sentiment prediction in financial headlines and analyzes the impact of evaluation metrics on performance.

## Key findings

- LSTM outperforms SVR by 4-6% in sentiment prediction.
- Using finance-specific embeddings improves model accuracy.
- Evaluation metrics significantly influence model assessment.

## 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.