Exploring BERT and Bi-LSTM for Toxic Comment Classification: A Comparative Analysis
- Title
- Exploring BERT and Bi-LSTM for Toxic Comment Classification: A Comparative Analysis
- Creator
- Tarun V.G.; Sivasakthivel R.; Ramar G.; Rajagopal M.; Sivaraman G.
- Description
- This study analyzes on the classification of toxic comments in online conversations using advanced natural language processing (NLP) techniques. Leveraging advanced natural language processing (NLP) techniques and classification models, including BERT and Bi-LSTM models to classify comments into 6 types of toxicity: toxic, obscene, threat, insult, severe toxic and identity hate. The study achieves competitive performance. Specifically, fine-tuning BERT using TensorFlow and Hugging Face Transformers resulted in an AUC ROC rate of 98.23%, while LSTM yielded a binary accuracy of 96.07%. The results demonstrate the effectiveness of using transformer-based models like BERT for toxicity classification in text data. The study discusses the methodology, model architectures, and evaluation metrics, highlighting the effectiveness of each approach in identifying and classifying toxic language. Additionally, the paper discusses the implementation of a userfriendly interface for real-time toxic comment detection, leveraging the trained models for efficient moderation of online content. 2024 IEEE.
- Source
- 2nd IEEE International Conference on Data Science and Information System, ICDSIS 2024
- Date
- 2024-01-01
- Publisher
- Institute of Electrical and Electronics Engineers Inc.
- Subject
- BERT; bi-LSTM; natural language processing; toxicity
- Coverage
- Tarun V.G., Christ (Deemed To Be University), School Of Sciences, Department Of Computer Science, Karnataka, Bengaluru, India; Sivasakthivel R., Christ (Deemed To Be University), School Of Sciences, Department Of Computer Science, Karnataka, Bengaluru, India; Ramar G., Christ (Deemed To Be University), School Of Sciences, Department Of Computer Science, Karnataka, Bengaluru, India; Rajagopal M., Christ (Deemed To Be University), School Of Business And Management, Department Of Lean Operations And Systems, Karnataka, Bengaluru, India; Sivaraman G., M.G.R. College, Department Of Computer Science, Tamil Nadu, Hosur, India
- Rights
- Restricted Access
- Relation
- ISBN: 979-835038166-5
- Format
- Online
- Language
- English
- Type
- Conference paper
Collection
Citation
Tarun V.G.; Sivasakthivel R.; Ramar G.; Rajagopal M.; Sivaraman G., “Exploring BERT and Bi-LSTM for Toxic Comment Classification: A Comparative Analysis,” CHRIST (Deemed To Be University) Institutional Repository, accessed February 28, 2025, https://archives.christuniversity.in/items/show/19335.