Semantic and Contextual Modeling for Malicious Comment Detection with BERT-BiLSTM
It addresses the issue of false and harmful content on social media platforms, providing a technical means for content moderation, but it is incremental as it combines existing methods.
This study tackled the problem of detecting malicious comments on social media by proposing a BERT-BiLSTM model, which achieved a precision of 0.94, recall of 0.93, and accuracy of 0.94 on the Jigsaw dataset, outperforming other models like standalone BERT and TextCNN.
This study aims to develop an efficient and accurate model for detecting malicious comments, addressing the increasingly severe issue of false and harmful content on social media platforms. We propose a deep learning model that combines BERT and BiLSTM. The BERT model, through pre-training, captures deep semantic features of text, while the BiLSTM network excels at processing sequential data and can further model the contextual dependencies of text. Experimental results on the Jigsaw Unintended Bias in Toxicity Classification dataset demonstrate that the BERT+BiLSTM model achieves superior performance in malicious comment detection tasks, with a precision of 0.94, recall of 0.93, and accuracy of 0.94. This surpasses other models, including standalone BERT, TextCNN, TextRNN, and traditional machine learning algorithms using TF-IDF features. These results confirm the superiority of the BERT+BiLSTM model in handling imbalanced data and capturing deep semantic features of malicious comments, providing an effective technical means for social media content moderation and online environment purification.