结合交叉注意力的双通道恶意评论识别方法OA北大核心CSTPCD

The detection of malicious comments is essentially a text classification problem.Compared to typical text classification,malicious comments are often accompanied by more subtle and unpredictable expressions,which results in low identification accura-cy,poor recognition effect,and inability to satisfy demands.To tackle the aforementioned issues,this paper proposes a two-channel text classification network combined with cross-attention mechanism(CA2TC),which employs graph convolutional network(GCN)and bidirectional long short-term memory(BiLSTM)to generate two distinct texts.Contextual feature information,as well as two distinct feature information,may be used to better explain the meaning of the text from numerous viewpoints.The suggested cross-attention approach improves and combines text characteristics gathered from two channels.Finally,the corrected features are concat-enated and transmitted to softmax through the fully connected layer for classification.The malicious comment data acquired from Weibo is utilized in this study to validate the suggested strategy.The experimental results show that,compared to some mainstream classification models,the proposed model has a better recognition effect,with classification accuracy increasing by 1.06%to 2.89%.The CA2TC model can fully extract the text features of malicious comments,leading effectively identify malicious comments.