Abstract
With the increase of social technology, e-commerce platforms have entered a period of rapid development. Improving customer satisfaction and purchase rate is the key to the survival of e-commerce platforms. Text mining and analysis of customer evaluation data will help to grasp the focus of customers and optimize the e- commerce platform. To this end, through text mining technology, the text comment data of five e-commerce platforms such as Amazon, eBay, Alibaba, Jingdong, and Taobao are collected, and the cleaned text is analyzed by particle swarm algorithm (PSO)-long short-term memory (LSTM) model. The data is subject to time scale extraction, and the extraction results are visualized and interpreted. The research shows that the logistics, price, freshness, quality and packaging of e-commerce platform merchants are important factors that affect the evaluation of e-commerce customer satisfaction.
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