Shopping review information is a comprehensive evaluation of the quality, price, after-sales service and other dimensions of the product after the consumer purchases the product. This information implies the consumer's satisfaction with a certain product and emotion for a type of product. This plays a vital role in the later product push of merchants and private customization services. Therefore, it is particularly important to intelligently analyze consumer evaluation data and perform fine-grained sentiment analysis on product characteristics to help users understand a large number of consumer evaluation data. Therefore, in this paper, combined with the data background, in the implementation process of SVM, the threshold between the support vector and the point vector is fully utilized for denoising, which improves the accuracy of the system. In terms of operation, the calculation time is saved by decomposing the original point vector, and the time efficiency is improved.
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