| Online review sentiment analysis is a novel field of Web data mining, which hasrecently attracted attentions of the researchers in computer science, economics,management and other disciplines. Researching how the sentiment factors of the onlineproduct reviews affect consumers’ buying behaviors and the related products sales, canmake online marketers predict more accurate market demand, and then they can set thestocking quantity according to the market demand forecast to get the best returns. So ithas important practical significance and great commercial value to the networkbusinessmen.The paper first introduces the research background and significance, summarizesthe domestic and foreign study situation and current problems, about review sentimentfactors analysis, online shopping and commodity stock. Secondly, the paper builds acomprehensive online shopping customer satisfaction evaluation index system todetermine the customer satisfaction coefficient. After the investigation of the onlineproducts reviews, most of the customers give the product reviews from product quality,service quality and delivery quality. The paper builds the comprehensive customersatisfaction evaluation index system from the three respects. It uses FAHP to determinethe index weight. According to the weights, it obtains the comprehensive customerssatisfaction coefficient. Again, it introduces the coefficient into the newsboy model, andconstructs a stocking model. It gives a correction to the order quantity in the newsboymodel which will be the stocking quantity. Finally, it makes Tmall women’s apparel anexample, obtains information from products reviews to extract the data. The indexsystem can help the marketers evaluate the emotional opinions of the reviews, and workout a plan closer to the actual sales to reduce the loss of business. It can help themarketers find their problems and improve themselves. The paper puts forward relevantcountermeasures and suggestions for network marketers to improve at last. |
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