We investigate whether pre-trained bidirectional transformers with sentiment and emotion information improve stance detection in long discussions of contemporary issues. As a part of this work, we create a novel stance detection dataset covering 419 different controversial issues and their related pros and cons collected by procon.org in nonpartisan format. Experimental results show that a shallow recurrent neural network with sentiment or emotion information can reach competitive results compared to fine-tuned BERT with 20x fewer parameters. We also use a simple approach that explains which input phrases contribute to stance detection.
@article{arxiv.2006.00052,
title = {Stance Prediction for Contemporary Issues: Data and Experiments},
author = {Marjan Hosseinia and Eduard Dragut and Arjun Mukherjee},
journal= {arXiv preprint arXiv:2006.00052},
year = {2020}
}