Xunyi Ren
ABSTRACT
Deep learning neural networks have significantly improved the detection rate of false comments, but their processing of text is still serializable, and there are still shortcomings in addressing the implicit connections between false comments. Therefore, a false comment detection model based on CNN GRU is proposed. For this model, first analyze the characteristics of false comments in real websites, preprocess the dataset, and then use Doc2vec to extract word vectors from the text, so that the input data can be combined with contextual context and preserve word order information. Then, CNN is used for text feature representation, GRU is introduced for comment classification, and the final CNN GRU model is constructed. By comparing the performance of models with different parameters in false comment detection through experiments, the most suitable model structural parameters are obtained. Finally, a performance comparison was conducted with commonly used machine learning algorithms and neural network models to verify the effectiveness of the CNN-GRU model.
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Index Terms
- Research on False Comment Detection Model Based on the Fusion of Convolutional Neural Network and GRU
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