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.

Key words: sentiment enhancement, Generative Adversarial Networks (GAN), fake comment detection, social network comment, BERT

摘要：

针对当前虚假评论检测模型存在的深层情感特征挖掘不足、语义依赖关系缺失以及泛化性能不佳的问题, 提出一种基于情感加权BERT与多任务对抗学习的虚假评论识别模型DEBR-GAN。首先, 借助情感词典辅助预训练BERT, 通过情感加权机制对评论文本中的潜在情感信息进行提取, 从而增强对评论中细微情绪变化的捕捉能力; 随后, 采用循环神经网络(RNN)对BERT输出的语义特征进行处理, 充分挖掘评论中词语之间的时序依赖及上下文关系, 以提高对文本细节的敏感性; 接着, 为提升模型在多领域场景下的鲁棒性与泛化能力, DEBR-GAN借鉴了生成对抗网络(GAN)的对抗学习思想, 将虚假评论检测器视为特征生成器, 用于提取跨领域共享的有效特征, 同时, 通过设置类别鉴别器和评分鉴别器, 在反向传播过程中采用梯度反转技术, 与生成器进行对抗博弈, 有效消除类别信息和用户评分偏好对特征提取过程的干扰, 从而保证检测器在识别虚假评论时具有高准确性。实验结果表明, 在大众点评数据集上, DEBR-GAN模型的F1值高达0.926, 与未引入多任务对抗学习模块的模型相比, 其分类准确率提高了5.1百分点, 而相较于当前最佳基线模型则提升了3.51百分点。此外, 该模型在处理不同情感倾向和语义结构的评论时均表现出较高的识别准确率, 充分验证了情感增强与对抗学习相结合在虚假评论检测中的有效性与优越性。

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, BERT