Attention-Based Bi-LSTM Network for Abusive Language Detection

Nelatoori, Kiran Babu; Kommanti, Hima Bindu

doi:10.1080/03772063.2022.2034534

Cited by 8 publications

(1 citation statement)

References 26 publications

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“…While attention-based transformers have been widely successful, their application to toxic comments classification is relatively recent. Studies have shown that these models can effectively capture the contextual nuances of toxic language, thereby achieving better performance compared to traditional machine learning methods [11,14]. However, it is essential to perform a thorough comparative analysis to understand the specific benefits and limitations of attention-based transformers in this domain.…”

Section: Attention-based Transformer Networkmentioning

confidence: 99%

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

Wang,

Chen

2023

JSMHES

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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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Section: Attention-based Transformer Networkmentioning

confidence: 99%