Consumer evaluation using machine learning for the predictive analysis of consumer purchase indicators

With the rapid development of the current network platform for online e-commerce, in addition to transparent price competition, buyer feedback also has a reasonable influence on consumers' purchasing decisions. Today, we can see that the feedback behavior of consumers on related websites, including well-known online shopping platforms such as Amazon Shopping, Shopee Shopping and Taobao, has been gradually strengthened in recent years. Whether substantive recommendations from consumer feedback help other superficial consumers read them to improve their shopping habits. In this study, we automatically classify feedback comments using machine learning, and monitor the growth trend of shopping transaction volume, selecting the Shopee shopping platform as an experimental case. The suggestions provided by customers based on reviews are incorporated into the sentiment word management analysis, and words and word scores are weighted. Finally, a shopping engine is built that simulates consumer behavior, filters variable factors using review management, and optimizes metrics for predicting consumer shopping.