Online Reviews Of Cars' Mining And Outlier Detection Based On Machine Learning

As for the e-commerce in the context of Web 2.0 with information overload,how to deal with a lot of online WOM,and rapidly and accurately obtain the knowledge that help customers make satisfied purchase decision or assist suppliers improve products and services has increasingly become the focus of the industry and academia.According to the demand,taking high-valued and professional automobiles with immature Internet sales mode as the research object,taking the data of online WOM of Audi Q5 as an example,the paper aims to solve three problems: 1)as a specific and mature product,what are the advantages and disadvantages of Audi Q5? 2)Can online WOM reflect the real emotion of customers when purchasing and using a product? If it can,how does it reflect that? 3)How to find outliers in online WOM and analyze the features and formation mechanism of these points to generate heterogeneous knowledge? The logic of the paper is as follows: first,after NLP processing,all product attributes were ordered according to customers' collective opinions to clarify the advantages and disadvantages of products and obtain the basic data set necessary for further study.In addition,in the face of the dispute among academia about usefulness of online WOM,the paper emphasized that online WOM was valuable only when it reflected the real emotion of customers when purchasing and using a product.Accordingly,on the basis of the basic data set that formed during answering the first question {opinion holder,product attributes,clause,polarity},two indicators were designed,namely individual negative review and modified individual negative review,so as to find the method of evaluating the real emotion of customers online through multiple linear regression.This proved that it was reliable to evaluate the advantages and disadvantages of products with explicit online comment data.Meanwhile,it also laid a foundation for constructing effective coordinate space and further find heterogeneous knowledge.Finally,from the perspective of outlier,exception mining was implemented to the data of WOM in two coordinate spaces,that is,"individual-collective opinion" and "comment-evaluation",so as to comb the features of outlier samples and obtain heterogeneous knowledge.The main practical contribution of the paper was that it clarified the advantages and disadvantages of different dimensions of Audi Q5,which provided a beneficial reference for customers to make a purchase decision and suppliers to improve products.Moreover,from the perspective of outlier,it was found that discontent emotion of heterogeneous WOM was significantly positively correlated to fuel consumption of automobile,which provided the possibility for practitioners to explore new law of customers' behavior.From the perspective of managers,the primary theoretical contribution of the paper was that on the basis of common knowledge discovery framework,the process of finding heterogeneous knowledge of online WOM was proposed through mature outlier test and knowledge classification.From the perspective of technology,the method that calculating individual comment emotion by taking the collective negative comment degree of product attributes rather than modifier dictionary as the weight can reduce the subjectivity of emotion and significantly and stably describe customers' real emotion.