False Comment Detection Model Based on Sentiment-Enhanced BERT and Multi-Task Generative Adversarial Networks

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.