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

Bachelor thesis (2021)

Authors

F.J. Roeters van Lennep Electrical Engineering, Mathematics and Computer Science

Contributors

P.K. Murukannaiah Interactive Intelligence - EEMCS (mentor)
Ricardo Marroquim Computer Graphics and Visualisation - EEMCS (graduation committee member)
Faculty
Electrical Engineering, Mathematics and Computer Science, Electrical Engineering, Mathematics and Computer Science
Copyright: © 2021 Jacob Roeters van Lennep
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Published Date

01-07-2021

Language

English

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Faculty

Electrical Engineering, Mathematics and Computer Science

Abstract

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.