Study On The Influencing Factors Of User Satisfaction Based On Text Mining

In recent years,the rapid development of China's Internet technology,various e-commerce sites have mushroomed and continue to rise.The rise of e-commerce websites has exerted a great influence on people's shopping patterns.More and more users choose to shop through e-commerce websites.As of June 2016,the number of online shopping users in China has exceeded 400 million.The way of buying goods through e-commerce websites not only saves users time and costs,but also consumers can express their opinions through e-commerce websites.This not only provides reference for other consumers,but also provides a lot of Valuable useful information.In order to improve the satisfaction of the user's product experience,it is of great significance to research and analyze the user's comment data.In the face of the geometric growth of e-commerce comment text information,we need to use text mining technology to automate the processing of massive amounts of data.Due to the particularity and complexity of Chinese language,mining potential information in Chinese review texts is a research difficulty in the field of natural language processing(NLP).It also involves many fields such as machine learning and artificial intelligence.Based on the factors affecting e-commerce satisfaction and the current research status of online text mining,this paper uses the existing research results to learn from Huawei's self-operated official flagship store mate9 mobile phone review data as an example to apply text mining technology to e-commerce satisfaction.Influencing factors in the field of research.Firstly,3,000 pieces of product review data of Huawei's Jetta own official flagship store mate9 mobile phone were automatically captured using web crawler technology.The captured data was processed in sequence of noise,jieba word segmentation,and stop word;secondly,feature words were selected from data processing results.,And adopts k-means clustering algorithm to cluster feature words,clustering results and influencing factors of user satisfaction;Finally,constructing a dictionary to rank the matching factors of user satisfaction factors,according to the experimental results of Jingdong Mall and Huawei mobile phone suppliers make corresponding suggestions.This study can quickly and timely extract the influencing factors of user satisfaction from the user review data.It can provide timely and effective reference for enterprise decision makers.It is also an innovation in the application of text mining technology.It is believed that with the development of text mining technology,Will bring greater value to the business.