User Demand Analysis Of Experience Good Based On Online Reviews Text Mining

With the further development of Web2.0 and the further improvement of the popularity of the Internet,the e-commerce industry comes with the Internet.The convenience of the information age enables users to buy satisfactory goods at home.Ecommerce platforms have sprung up.With the improvement of information technology,e-commerce enterprises are constantly optimizing their own platforms to meet the shopping needs of consumers.With the development of mobile payment and the optimization and upgrading of express industry,more and more consumers choose online shopping instead of offline shopping.With the opening and sharing of the Internet and the continuous user centered adjustment and upgrading of e-commerce platform,a large number of user generated content online reviews are generated along with online shopping.The concept of "online reviews" has been widely discussed by many scholars since it was put forward.Many scholars have explained the concept of "online reviews" on the basis of their own research and understanding,but so far the meaning of "online reviews" has not been unified in the academic community.But this does not prevent scholars from discussing the value of online reviews.Online reviews are actually the comments on goods published by users on shopping websites.While users share their online shopping experience,they also share their needs for products and services.Therefore,there is great value hidden in online reviews.For enterprises,online reviews are an important channel and means of mining user needs,finding the right market positioning,and optimizing and upgrading products according to user needs.At present,although the existing research on user needs is more,but more on the construction of user needs model,product feature extraction method and the importance of user needs analysis,and the research on the use of online review text mining for user needs analysis is less.In this paper,we use text mining method,according to the commonly accepted product classification method,take the typical experiential product mask as an example,and use Mask online reviews as data sources to mine user needs.A crawler tool,octopus collector,is used to crawl the online reviews of Jingdong platform mask.After data cleaning,text mining is carried out on effective online reviews.Firstly,the text is preprocessed,and the word is segmented by Jieba,and the word frequency is counted;Then,the LDA topic model is used to cluster the processed text;Finally,according to the results of text mining,the user needs are summarized,and the composition of user needs elements is obtained.The questionnaire survey method is used to further verify the composition of user needs elements obtained by online comment text mining.In addition,for different types of mask,users focus on the specific requirements.Through the online comment text mining and the empirical analysis of questionnaire survey,the user needs can be basically obtained.The results show that the user needs can be roughly divided into two categories: products and services.The product mainly involves product efficacy,including basic effect and functional effect;product properties,including product texture,product taste and appearance;the product use feeling,including its ease of use,absorption and applicability;product price,including discount and price ratio;and product brand,including brand trust and brand evaluation.The service mainly involves logistics service,including the receiving time and package integrity;customer service,including immediate feedback,attitude.In addition,this study also found that the demand for different types of mask was different.At the end of this paper,the author gives some suggestions to enterprises,e-commerce platforms and users according to the user needs,including suggestions that enterprises attach importance to the efficiency of products and their own reputation;it is suggested that e-commerce platform strictly implement the "last kilometer",and pay attention to the regular training of customer service personnel;it is recommended that users firstly perform skin tests before using mask,and accurately grasp their skin types and pay attention to the communication with custer service before using mask.