Identification of online harassment using ensemble fine-tuned pre-trained Bert
Identification of online hate is the prime concern for natural language processing researchers; social media has augmented this menace by providing a virtual platform for online harassment. This study identifies online harassment using the trolling aggression and cyber-bullying dataset from shared tasks workshop. This work concentrates on extreme pre-processing and ensemble approach for model building; this study also considers the existing algorithms like the random forest, logistic regression, multinomial Naïve Bayes. Logistic regression proves to be more efficient with the highest accuracy of 57.91%. Ensemble bidirectional encoder representation from transformers showed promising results with 62% precision, which is better than most existing models.
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Hivatkozás
MLA: Ganie, Aadil Gani, and Samad Dadvandipour. "Identification of online harassment using ensemble fine-tuned pre-trained Bert." Pollack Periodica (2022).
APA: Ganie, A. G., & Dadvandipour, S. (2022). Identification of online harassment using ensemble fine-tuned pre-trained Bert. Pollack Periodica.
ISO690: GANIE, Aadil Gani; DADVANDIPOUR, Samad. Identification of online harassment using ensemble fine-tuned pre-trained Bert. Pollack Periodica, 2022.
BibTeX:
@article{ganie2022identification,
title={Identification of online harassment using ensemble fine-tuned pre-trained Bert},
author={Ganie, Aadil Gani and Dadvandipour, Samad},
journal={Pollack Periodica},
year={2022},
publisher={Akad{'e}miai Kiad{'o} Budapest}
}