A Study About Users Research On Electric Bicycle Based On Text Mining And Experience Modeling

As the first step in the product development cycle,user research guides the overall trend of the product and is of great significance.However,traditional user research methods have disadvantages such as high practical cost,small sample size,and low timeliness.Feedback on product development There are limitations.In the era of Internet big data,massive amounts of data contain a large amount of user information,which can accurately outline user needs and provide effective guidance for product design,production and sales.Therefore,how to effectively conduct data mining analysis and feed back to actual product research and development is of great significance to current product iteration and industry development.This topic is based on text mining related theories and technologies,introduces user experience thinking,chooses electric bicycles as the research carrier,and builds a complete user experience model and extended vocabulary based on the massive consumption and shopping data and evaluation texts of the e-commerce platform.Multi-dimensional analysis and element association analysis of a single element of a product provide new ideas and new methods for user research based on text mining,and provide better feedback on product design,production and sales.The main work of this subject includes the following three aspects:First,build an electric bicycle user experience model.Starting from the experience level,through product-related material review,product text data mining analysis,including LDA topic model extraction and TF-IDF text key information extraction,construct the electric bicycle user experience model architecture;based on the experience model architecture,use the Word2 vec algorithm to locate similar entries Expansion of the model-derived lexicon,and constructing the core model of this subject for mining and analysis-the electric bicycle user experience model.Second,conduct product single element mining analysis,and propose a product single element analysis method model based on e-commerce platform text mining.Based on the information characteristics of the e-commerce platform,the analysis dimensions are divided;based on the electric bicycle user experience model,combined with the user evaluation text characteristics to select the target element.Analyze and compare multi-dimensional user research conclusions such as industry product user models,product price characteristics influence trends,and competitive product analysis conclusions,and maximize the mining results based on the information sources of the e-commerce platform.Make up for the shortcomings of traditional user research methods and provide practical references for product design,production and sales.Third,carry out product element correlation analysis,and propose a goaloriented product element correlation analysis process method.In terms of implementation technology,the document-term matrix is constructed through text vectorization,and the product element association analysis is performed on the product based on the Apriori algorithm;in the analysis structure,the target population and user goals are selected as the guiding target to obtain deeper user research information.Build user portraits in different dimensions to achieve developmental guidance for product design,production and sales.