Research On The Impact Of Additional Comments On The Sales Of Beauty Products On E-commerce Platforms

At present,China Mobile Internet is developing at a faster pace.,and the scale of transactions of beauty products through online channels such as e-commerce platforms has increased significantly.Due to the particularity of beauty products,the understanding of real quality often needs to go through the test of time.Consumers often post the effects of using the product for a period of time in additional comments.Therefore,adding online comments with additional comments will have a certain effect on consumer online purchases,as well as sales of beauty products.This thesis first summarizes the psychology and behavioral characteristics of beauty makeup,and selects the corresponding factors from the review dimension,shop dimension,consumer dimension and product feature dimension to make research hypotheses,and builds the influence model of beauty product sales based on ELM model.Tmall Mall’s beauty product review data for multiple regression analysis,adjustment effect analysis.The study found that commentary emotions,number of reviews,and store reputation all had a significant positive impact on product online sales,while the number of reviews negatively affected product sales.The study confirms that the consumer’s membership level will have a regulatory effect on the sales of beauty products.The average consumer has a more trusting attitude towards the comments posted by users with higher membership levels,making it easier to make purchasing decisions.Secondly,in the study of influencing factors,this thesis finds that additional comments have a certain impact on sales.In order to further study the additional commentary emotions,we first explain the emotional convergence and contradiction between the initial comment and the additional comment,when there is a positive-positive append combination.Consumers’ impressions of comments are positive;when negative-negative additional combinations occur,the impression of the comments is negative.For online reviews of positive-negative additional combinations,consumers are more likely to be negatively reviewed for content in making purchase decisions.For online reviews of negative-positive appends,although additional comments express positive emotions,negative emotions in the initial comments have a greater impact on consumers.This thesis uses text mining technology to analyze the emotional tendency and intensity of online comments.According to the different situations of comment convergence and difference phenomenon,this thesis proposes an emotional intensity correction strategy based on additional comments.Finally,this thesis uses the optimized BP neural network to construct a sales forecast model for beauty products.For the important index of commenting emotions,two kinds of data based on the additional comment emotion correction value and the comment feature emotion correction value are selected and applied to the prediction model,and the sales and comment data of 10 Tmall Mall beauty products are selected for nearly one year.The test and verification results show that the use of emotional strength correction based on additional comments can effectively improve the accuracy of the sales forecast of beauty products.This thesis also proposes management inspiration and suggestions from three aspects: improving the richness of online comments,attaching to the emotion of additional comments,and enriching the indicators of predictive models.