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Article Open Access September 13, 2023

A Comparative Study of Attention-Based Transformer Networks and Traditional Machine Learning Methods for Toxic Comments Classification

Sihao Wang 1,* and Bingjie Chen 2
1
Department of Mathematics, Southern Methodist University, Dallas, TX, United States
2
Department of Computer Science, University of York, York, United Kingdom
Publihed in: Current Research in Public Health (Volume 1, Issue 1, 2023)
Page(s): 22-30
DOI: 10.31586/jsmhes.2023.697
Received
May 14, 2023
Revised
July 15, 2023
Accepted
August 29, 2023
Published
September 13, 2023
Keywords
Text classification; Attention networks; Deep Learning; Natural Language Processing; Supervised Learning
Creative Commons

This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.
Copyright: Copyright © The Author(s), 2023. Published by Scientific Publications
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Cite This Article

APA Style
Wang, S. , & Chen, B. (2023). A Comparative Study of Attention-Based Transformer Networks and Traditional Machine Learning Methods for Toxic Comments Classification. Current Research in Public Health, 1(1), 22-30. https://doi.org/10.31586/jsmhes.2023.697
ACS Style
Wang, S. ; Chen, B. A Comparative Study of Attention-Based Transformer Networks and Traditional Machine Learning Methods for Toxic Comments Classification. Current Research in Public Health 2023 1(1), 22-30. https://doi.org/10.31586/jsmhes.2023.697
Chicago/Turabian Style
Wang, Sihao, and Bingjie Chen. 2023. "A Comparative Study of Attention-Based Transformer Networks and Traditional Machine Learning Methods for Toxic Comments Classification". Current Research in Public Health 1, no. 1: 22-30. https://doi.org/10.31586/jsmhes.2023.697
AMA Style
Wang S, Chen B. A Comparative Study of Attention-Based Transformer Networks and Traditional Machine Learning Methods for Toxic Comments Classification. Current Research in Public Health. 2023; 1(1):22-30. https://doi.org/10.31586/jsmhes.2023.697 
@Article{crph697,
AUTHOR = {Wang, Sihao and Chen, Bingjie},
TITLE = {A Comparative Study of Attention-Based Transformer Networks and Traditional Machine Learning Methods for Toxic Comments Classification},
JOURNAL = {Current Research in Public Health},
VOLUME = {1},
YEAR = {2023},
NUMBER = {1},
PAGES = {22-30},
URL = {https://www.scipublications.com/journal/index.php/JSMHES/article/view/697},
ISSN = {2831-5162},
DOI = {10.31586/jsmhes.2023.697},
ABSTRACT = {With the rapid growth of online communication platforms, the identification and management of toxic comments have become crucial in maintaining a healthy online environment. Various machine learning approaches have been employed to tackle this problem, ranging from traditional models to more recent attention-based transformer networks. This paper aims to compare the performance of attention-based transformer networks with several traditional machine learning methods for toxic comments classification. We present an in-depth analysis and evaluation of these methods using a common benchmark dataset. The experimental results demonstrate the strengths and limitations of each approach, shedding light on the suitability and efficacy of attention-based transformers in this domain.},
}
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AB  - With the rapid growth of online communication platforms, the identification and management of toxic comments have become crucial in maintaining a healthy online environment. Various machine learning approaches have been employed to tackle this problem, ranging from traditional models to more recent attention-based transformer networks. This paper aims to compare the performance of attention-based transformer networks with several traditional machine learning methods for toxic comments classification. We present an in-depth analysis and evaluation of these methods using a common benchmark dataset. The experimental results demonstrate the strengths and limitations of each approach, shedding light on the suitability and efficacy of attention-based transformers in this domain.
DO  - A Comparative Study of Attention-Based Transformer Networks and Traditional Machine Learning Methods for Toxic Comments Classification
TI  - 10.31586/jsmhes.2023.697
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