计算机工程2026，Vol.52Issue(4)：276-289,14.DOI:10.19678/j.issn.1000-3428.0252154
基于情感增强BERT与多任务生成对抗网络的虚假评论检测模型
False Comment Detection Model Based on Sentiment-Enhanced BERT and Multi-Task Generative Adversarial Networks
摘要
Abstract
Current false comment detection models face several problems such as insufficient mining of deep emotional features,lack of semantic dependency relationships,and poor generalization performance.In response to these,a false comment recognition model,DEBR-GAN,based on emotion-weighted BERT and multi-task adversarial learning,is proposed.First,using an emotion dictionary to assist in pretraining BERT,the potential emotional information in the comment text is extracted through an emotion weighting mechanism,thereby enhancing the ability to capture subtle emotional changes in the comments.Subsequently,a Recurrent Neural Network(RNN)is used to process the semantic features output by BERT,fully exploring the temporal dependencies and contextual relationships between words in comments for improving sensitivity to text details.Furthermore,to enhance the robustness and generalization ability of the model in multi-domain scenarios,DEBR-GAN draws on the adversarial learning concept of the Generative Adversarial Networks(GAN),treating the fake comment detector as a feature generator for extracting effective features shared across domains.Simultaneously,by setting category discriminators and rating discriminators,gradient reversal techniques are used in the backpropagation process to engage in adversarial games with the generator.This effectively eliminates the interference of category information and user rating preferences in the feature extraction process,thereby ensuring that the detector is highly accurate in identifying fake comments.The experimental results show that,on the Dianping dataset,the F1 value of the DEBR-GAN model is as high as 0.926.Compared with those of the model without the multi-task adversarial learning module and the current best baseline model,the classification accuracy of DEBR-GAN is increased by 5.1 and 3.51 percentage points,respectively.In addition,DEBR-GAN exhibits high recognition accuracy in handling comments with different emotional tendencies and semantic structures,thereby verifying the effectiveness and superiority of combining emotional enhancement and adversarial learning in false comment detection.关键词
情感增强/生成对抗网络/虚假评论检测/社交网络评论/BERTKey words
sentiment enhancement/Generative Adversarial Networks(GAN)/fake comment detection/social network comment/BERT分类
信息技术与安全科学引用本文复制引用
李丹,谢语涵,韩潇帅,吕晨..基于情感增强BERT与多任务生成对抗网络的虚假评论检测模型[J].计算机工程,2026,52(4):276-289,14.基金项目
国家自然科学基金(62476164) （62476164）
教育部人文社会科学研究课题(24YJCZH197). （24YJCZH197）
