Deep Learning for Stance Detection: A Review and Comparison of the State-of-the-Art Approaches

Deep Learning for Stance Detection: A Review and Comparison of the State-of-the-Art Approaches

Roeters van Lennep, Jacob (TU Delft Electrical Engineering, Mathematics and Computer Science; TU Delft Intelligent Systems)

Murukannaiah, P.K. (mentor)
Marroquim, Ricardo (graduation committee)

Delft University of Technology

Computer Science and Engineering

CSE3000 Research Project

2021-07-01

Stance detection is a Natural Language Processing task that can detect if the input text is in favour, against or neutral towards a target. Research on stance detection has been growing and evolving over the last decade. In this paper, the current approaches for stance detection are discussed with a focus on the deep learning approaches. The organized competitions are discussed, and the most used traditional and deep learning approaches are shown. The challenges that arise with deep learning approaches are looked into further. Finally, an experiment was performed to examine and demonstrate the effects of a small data set on various stance detection models, this was done using the SVM, CNN, and BERT models on the SemEval 2016 data set. This experiment shows that a smaller data set has a greater negative impact on the CNN model than theSVM model. BERT is affected the least and outperforms the other models significantly.

Stance Detection
Deep Learning

http://resolver.tudelft.nl/uuid:c1ac3460-60d7-4dd4-9ef6-dba7dd05a853

Student theses

bachelor thesis

© 2021 Jacob Roeters van Lennep