Research On Key Issue Mining Of Online Shopping Platforms Towards Products And Service

With the rapid development of E-commerce,online shopping platforms gather lots of sellers and provide customers various kinds of products.Under the background of larger online market and more severe competition,it is of necessity and practical meaning to study how online shopping platforms improve products and services,in order to improve the satisfaction and lyalty of customers.In the operation and development of onlne shopping platforms,large quantities of cumsuming-related data are created,including product reviews,customer complaint text and advertisement business data.These various kinds of data contain related information of consuming.This essay proposes solutions in the aspect of data mining and text mining to provide effective techniques,principles and methods.The research route of this essay is shown below.(1)Product reviews conain large quantites of noisy and irrelevant words,resulting in a lot of noisy and irrelevant contents.Feature selection methods are capable of selecting keywords which are related to advantages and disadvantages of products to reduce much noise for generating produt review adstracts.(2)Facing large quanties of product reviews,sellers need to spend a lot of time to obtain importat information of products.The method for generating product review abstracts is able to generate clear and readable abstracts to solve the problem of information explosion.(3)The optimization of online shopping processes is helpful to raise purchasing experiences of customers,regulate seller management and reduce purchasing conflicts.However,current researuches which are related to service quality of online shopping platforms generally focus on the whole platforms,ignoring the existing problems and optimizatio n strategies of online shopping processes.Based on customer complaint text,the optimization method for online shopping processes combining text mining and service science model is capable of avoiding the problems of experience limitation,lack of samples and data biases on the one hand,and reflecting the most customer-concerned service problems.(4)The display of ineffective advertisements brings far less profits compared to the ir costs,so it is urgent to find the solution for raising the recognition rates of effective advertisements and helping sellers discover and remove ineffective advertisements in time to save costs.Advertisement classification model based on objective business data,to some extence,is able to avoid the problems such as lack of data samples,data biases,strong subjectivity,quantification difficulties in current researches on advertisement effectivenss evaluation,in order to solve the problem of effective and ineffective advertisements classification.The research contents of this essay are shown below.(1)Two feature selection methods are designed based on two typical deep learning models,that is,convolutional neural network and long-short term memory network.To begin with,the specific principles of the two methods are described,including steps of performing feature selection,the structures and training of the deep learning models.Next,the two proposed methods are compared with traditional methods in the three aspects of classification performance,semantical performance and storage performance on their effectivenss,based on several public datasets.Finally,performance of the two proposed method s is compared based on the experimental results of classification performance,semantical performance and storage performance.(2)Method for generating product review abstracts is designed combining part-of-speech(POS)rules,feature selection method,topic model and deep learning model.Firstly,the specific principles of the method is described,including the setting of POS rules,the roles of feature selection method,topic model and deep learning model.Secondly,datasets of product reviews from web are used to perform case studies to validate effectiveness of the method.(3)Optimization method for online shopping processes is designed based on text mining and service science model.Firstly,the proposed method for generating product review abstracts is used to mine the existing problems of online shopping processes from customer complaint text.Secondly,the service science model ?process chain network ? is used to propose targeted optimization strategies,according to the mined specific problems of online shopping processes.(4)Advertisement classification model based on Gaussian filter and decision tree is proposed.Firstly,the specific principles of the model are descr ibed,including the affect of Gaussian filter on business data and the parameter settings of decisioin tree.Secondly,the effectiveness of the model is validated by using practical advertisement business data.The innovations of this essay are shown below.(1)The proposed methods based on deep learning provide new research route for current researches on feature selection.The deep learning models are used in feature selection to designed more effective methods by combining deep features and traditional term frequency information.Unsupervised training strategy is selected for the training of deep learning models to improve the applicability for unsupervised text.(2)The proposed method for generating produc t review abstracts combining advantages of several methods enriches the researches on current researches on product reviews mining.A three-level information extraction mechanism is designed to guarantee that the extracted information is able to reflect important contents of product reviews.An interactive mechanism is provided by feature selection for customers to select...