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

By Andrew Moore, Paul Rayson. 2017

16 citations recorded by the source corpus.

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.

Subfields: Artificial Intelligence, Management Science and Operations Research

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Omer Berat Sezer
2. M. Ugur Gudelek
3. André Freitas
4. Siegfried Handschuh
5. A. Ozbayoglu

In Management Science and Operations Research

1. M. Ugur Gudelek
2. Omer Berat Sezer
3. A. Ozbayoglu
4. André Freitas
5. Weiwei Jiang
6. Longbing Cao

Citations in the corpus

• Deep Learning for Financial Applications : A Survey (2020, methodology): “[194] used text mining techniques for sentiment analysis from the financial news.”
• SemEval-2017 Task 5: Fine-Grained Sentiment Analysis on Financial Microblogs and News (2017, methodology): “Moore and Rayson (2017) experimented with an ML and DL algorithm respectively, with the latter performing better.”