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

arXiv: https://arxiv.org/abs/1705.00571 · HTML: https://mlsanity.com/paper/1705.00571/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 May 2017.

## Built on by (most cited first, 3 of 3)

- [SEntFiN 1.0: Entity-Aware Sentiment Analysis for Financial News](https://mlsanity.com/paper/2305.12257/) (2023)
- [News Headline Grouping as a Challenging NLU Task](https://mlsanity.com/paper/2105.05391/) (2021)
- [Towards Financial Sentiment Analysis in a South African Landscape](https://mlsanity.com/paper/2106.10004/) (2021)

## Abstract (arXiv)

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.