Financial Aspect and Sentiment Predictions with Deep Neural Networks: An Ensemble Approach

By Guangyuan Piao, John Gerard Breslin. 2018

26 citations recorded by the source corpus.

In this paper, we describe our ensemble approach for sentiment and aspect predictions in the financial domain for a given text. This ensemble approach uses Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs) with a ridge regression and a voting strategy for sentiment and aspect predictions, and therefore, does not rely on any handcrafted feature. Based on 5-cross validation on the released training set, the results show that CNNs overall perform better than RNNs on both tasks, and the ensemble approach can boost the performance further by leveraging different types of deep learning approaches.

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. Dogu Araci
4. A. Ozbayoglu

In Management Science and Operations Research

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

Citations in the corpus

• Deep Learning for Financial Applications : A Survey (2020, methodology): “[192] Twitter posts, news headlines Sentences, text Deep-FASP Accuracy, MSE, R(2) -”
• FinBERT: Financial Sentiment Analysis with Pre-trained Language Models (2019, background): “It should be noted that the test set these two papers [31] [24] use is the official FiQA Task 1 test set.”