An Automated Toxicity Classification on Social Media Using LSTM and Word Embedding

The automated identification of toxicity in texts is a crucial area in text analysis since the social media world is replete with unfiltered content that ranges from mildly abusive to downright hateful. Researchers have found an unintended bias and unfairness caused by training datasets, which cause...

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Detalles Bibliográficos
Autores principales: Alsharef, Ahmad, Aggarwal, Karan, Sonia, Koundal, Deepika, Alyami, Hashem, Ameyed, Darine
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Hindawi 2022
Materias:
Research Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8863472/
https://www.ncbi.nlm.nih.gov/pubmed/35211168
http://dx.doi.org/10.1155/2022/8467349

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8863472/
https://www.ncbi.nlm.nih.gov/pubmed/35211168
http://dx.doi.org/10.1155/2022/8467349

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