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

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.

arXiv comment5 pages, to Appear in the Proceedings of the 11th International Workshop on Semantic Evaluation (SemEval 2017), August 2017, Vancouver, BC

References in corpus (4)

• Scikit-learn: Machine Learning in Python
• Efficient Estimation of Word Representations in Vector Space
• TensorFlow: Large-Scale Machine Learning on Heterogeneous Distributed Systems
• Thumbs Up or Thumbs Down? Semantic Orientation Applied to Unsupervised Classification of Reviews

Cited by in corpus (1)

• SEntFiN 1.0: Entity-Aware Sentiment Analysis for Financial News