Research And Design Of Fake Reviews Detection Based On Deep Learning

The existence of numerous fake reviews in e-commerce websites will not only affects the interests of consumers,but also hurts the reputation of the business,and it's not conducive to the development of e-commerce industry.Therefore,the detection technology of fake reviews is crucial to the sound development of e-commerce.Fake reviews detection can be regarded as a text classification problem,the core is to train classifiers based on classification features to distinguish truthful reviews and fake reviews.Deep learning networks have more hidden layers than traditional neural networks.In the learning process,features are extracted from low to high levels,and the weights between neurons are continuously modified and adjusted to establish the mapping between low-level features to high-level features.The paper focuses on the study of fake reviews detection based on deep learning,and designs a fake review detection system based on deep learning.The main work and innovation of this paper is as follows:(1)Fake reviews detection based on deep learning extraction features.After constructing and cleaning the Amazon dataset,a review word vector model was established.Convolutional neural network ? deep confidence network and LSTM network are used to extract the depth features of the review content texts,and the classification is combined with the support vector classifier.By comparing the traditional feature extraction method based on language model,the deep learning model can effectively extract the depth features of the content of the review,and the LSTM network is more suitable for extracting text features than convolutional neural network and deep confidence network.(2)Fake reviews detection based on fusion features.In order to improve the utilization of effective features,the features of the reviews content extracted by the deep learning model and the reviews behavior characteristics of the binary code representation are combined with the support vector machine for classification.Through experimental comparison,it is proved that the fusion of review behavior characteristics after detection can further improve the recall of the algorithm.(3)In order to further verify the effectiveness and feasibility of the proposed methods,we design and implement a fake reviews detection system based on deep learning.The experimental results including both content processing capability and the event detection accuracy of the system show that fake reviews can be detected effectively.