Abstract
The online store credit rating is a reflection of the seller's integrity and the quality of the product. The level of the credit rating directly affects the buyer's desire to purchase. Two important factors affecting the credit rating are data and models. The innovation of this research is that the collected data comes from the second evaluation, and the credit evaluation model is improved based on the snowNLP tool, and the malicious brushing filtering function is added. Compared with the credit evaluation system commonly used in current online stores, the evaluation results of the paper are more accurate, detailed and intuitive, and may effectively reduce false brushing and threat review.
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