Toxic Comments Classifications

This paper presents a deep learning approach for toxic comment classification using Bidirectional LSTM (Bi LSTM) in TensorFlow. The model identifies multiple categories of toxicity, including toxic, severe toxic, obscene, threat, insult, and identity hate. The dataset from Kaggle's Toxic Comment Classification Challenge is used for training and evaluation. Text vectorization is performed using both TF- IDF and TensorFlow's Text Vectorization layer. The system demonstrates efficient detection of toxic language, offering potential for real-time content moderation

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Publication Details

Journal
International Journal of Advanced Research in Science Communication and Technology
Published
2025-11-21
DOI
https://doi.org/10.48175/ijarsct-29979
Primary Topic
Hate Speech and Cyberbullying Detection
Type
article
Field-Weighted Citation Impact
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article

Toxic Comments Classifications

Prof. Parimal Kumar K R, Saqlain Ahmed
International Journal of Advanced Research in Science Communication and Technology
Hate Speech and Cyberbullying Detection
article

Toxic Comments Classifications

Prof. Parimal Kumar K R, Saqlain Ahmed
article en

Abstract

This paper presents a deep learning approach for toxic comment classification using Bidirectional LSTM (Bi LSTM) in TensorFlow. The model identifies multiple categories of toxicity, including toxic, severe toxic, obscene, threat, insult, and identity hate. The dataset from Kaggle's Toxic Comment Classification Challenge is used for training and evaluation. Text vectorization is performed using both TF- IDF and TensorFlow's Text Vectorization layer. The system demonstrates efficient detection of toxic language, offering potential for real-time content moderation

International Journal of Advanced Research in Science Communication and Technology
Institute of Engineering (NP)
Openalex Percentile: Top 81%
Hate Speech and Cyberbullying Detection
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