Preparing Stance Detection: Feature Extraction Methods and Their Performance Used for Feature-Based Machine Learning Algorithms

Bachelor thesis (2021)

Authors

K.K. Vass Electrical Engineering, Mathematics and Computer Science

Contributors

P.K. Murukannaiah Interactive Intelligence - EEMCS (supervisor 1)
Ricardo Marroquim Computer Graphics and Visualisation - EEMCS (supervisor 2)
Faculty
Electrical Engineering, Mathematics and Computer Science
Copyright: © 2021 Kristóf Vass
Natural Language Processing Machine Learning Stance Detection
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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 aims to detect the stance (support, agreement, or disagreement) of a piece of text towards some target. In this paper, we aim to find the best performing pair of feature extraction method and feature-based machine learning algorithm. By doing so, an explainable method can be found to show how to solve stance detection problems. After researching the most common techniques, twenty different combinations are evaluated. We have found that the best performing pair is Word N-gram used with Logistic Regression, which achieves an F-score of 0.599 and an accuracy of 0.66.