用BERT+BiLSTM提升恶意评论识别准确率
Semantic and Contextual Modeling for Malicious Comment Detection with BERT-BiLSTM
- 融合BERT语义理解与BiLSTM上下文建模
- 在Jigsaw数据集上达0.94精度、0.93召回率
- 适合需要高精度内容审核的平台应用
本研究旨在开发一种高效精准的恶意评论检测模型，以应对社交媒体平台上虚假有害内容日益严重的挑战。提出一种结合BERT与BiLSTM的深度学习模型：BERT通过预训练捕捉文本深层语义特征，BiLSTM擅长处理序列数据并进一步建模文本上下文依赖关系。在Jigsaw Unintended Bias in Toxicity Classification数据集上的实验表明，BERT+BiLSTM模型在恶意评论检测任务中表现优异，精确率为0.94，召回率为0.93，准确率为0.94，优于独立使用的BERT、TextCNN、TextRNN及基于TF-IDF特征的传统机器学习算法。结果证实该模型在处理不平衡数据和捕捉恶意评论深层语义特征方面具有显著优势，为社交媒体内容审核与网络环境净化提供了有效技术手段。
原文摘要 · Abstract (English)
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.
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