Research On User Demand Of Folding Electric Bicycle Based On Text Mining

There is no doubt that user demand research is the most basic and core part in the process of product innovation design and development.Under general situation,it depends what degree the product can meet the demand of users for the success of product research and development.In terms of user demand,it arises from people’s desire that can lead to user motivation and embody in the process of user experience,that is,the interaction between users and products or systems.Meanwhile,user experience is a dynamic process,the cognitive understanding,emotional attitude and behavior of users to products in the interaction process will change with the change of users,scenarios and product states,in which the corresponding user demand for products will also change.As a result,it can be seen that the research of user demand identification/mining is closely related to the process of user experience.In the context of developed Internet environment,the contents for the data of online user comments are rich,which also contain a lot of user information,thus being an important data source to obtain user demand.For the purpose of accurately and comprehensively obtaining user demand from online user reviews,it proposes user demand identification method by text mining based on user experience process in this paper,which comprehensively considers the features of "user-scenario-product" and their relationships.At the same time,it combines the method of sentiment analysis and association rule mining to identifie user demand,so as to provide data services for product development.As for the main work of this study,it includes the following aspects:(1)Building the research model.It starts from the level of experience to analyze the user experience process and its influencing factors,in which it also constructs the user demand identification model based on the user experience process.Through the combination with this model,it builds the core framework of this study,that is,the text mining user demand analysis framework based on user experience.(2)Build the ontology thesaurus of folding electric bicycle field.According to the text mining technology of the online shopping review,it combines the traditional research methods with data mining technology to build the ontology structure of folding electric bicycle field,and based on this,build the structure of folding electric bicycle field.Based on this,it constructs the ontological thesaurus,emotional thesaurus and sample feature thesaurus of this study for folding electric bicycle,which also establishes the hierarchical word list of sample feature attributes.(3)Research on product feature attribute mining based on sentiment analysis.It establishes the process framework of product feature attribute mining in line with emotion analysis.According to this framework,it matches the feature attribute labels,carries out the analysis of the emotion polarity of sample clause comments based on emotional thesaurus,which also makes the “feature and emotion” mapping for sample comment clauses.Besides,it puts forward a calculation method based on the feature emotion polarity of the comment users in this paper,which also makes statistics based on the frequency of feature mention as well as the “feature,emotion” mapping data of the comment users,so as to obtain more accurate user demand preferences.(4)Mining the emotional attitude and preference degree of users to product feature attributes based on user experience process by association rules.According to the user’s emotional score to each feature attribute of "user-scenario-product",it carries out the emotional attitude correlation analysis of "user-scenario-product" in this paper.On the other hand,it obtains the emotional attitude and preference degree of users to product feature attributes in the process of different user experiences through the specific mining of preference association rules between "user-scenario" and product feature attributes at all levels,so as to identify user demand,and put forward corresponding opinions for product design.