Research on Analysis of E-commerce Platform Product Reviews based on Sentiment Classification Model

With the rapid development of e-commerce, tremendous consumers choose to meet their own needs through online shopping, and they also put forward higher requirements for the quality of goods and after-sales service. As one of the largest e-commerce platforms in China, Taobao has a huge user base and rich user review data. For merchants, merchants can improve and opti-mize products based on the review data of these users, which can improve the quality and service level of goods. Therefore, it is of great significance to study the problems existing in Taobao user reviews. This paper uses the Py-thon programming language and the spark open-source framework to ana-lyze the sentiment of Taobao agricultural product reviews, and processes the review data through lexical analysis, semantic analysis and other methods, so as to obtain users' views on the problems in commodity information, logistics services, after-sales service, etc. From our extensive experiments, we can ob-serve that our proposed can achieve the acceptable classification accuracy for reviews.