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
- Editors:
- Steven Bethard, Marine Carpuat, Marianna Apidianaki, Saif M. Mohammad, Daniel Cer, David Jurgens
- Venue:
- SemEval
- SIG:
- SIGLEX
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 581–585
- Language:
- URL:
- https://aclanthology.org/S17-2095
- DOI:
- 10.18653/v1/S17-2095
- Cite (ACL):
- Andrew Moore and Paul Rayson. 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 Evaluation (SemEval-2017), pages 581–585, Vancouver, Canada. Association for Computational Linguistics.
- Cite (Informal):
- Lancaster A at SemEval-2017 Task 5: Evaluation metrics matter: predicting sentiment from financial news headlines (Moore & Rayson, SemEval 2017)
- PDF:
- https://preview.aclanthology.org/emnlp-22-attachments/S17-2095.pdf
- Code
- apmoore1/semeval