Research And Application About The Sentiment Classification Of Automobiles’ Online Reviews

Across the Web2.0world, we’re seeing a quiet-but-knee-jerk shift away fromUGC in favor of professional content, tracking and collecting data, makingmaximum use of this content is an assignment of relative enterprises. With the rapiddevelopment of Internet and informationization construction, the resources in thenet increases exponentially, it’s easilier to get the market feedback on Internent.That is, online reviews are not only utilized for consumer decision making, but alsofor the enterprise. However, how to increase return on investment of UGC, how tomake more productive is the concern of every enterprise. Automobile, as ahigh-price, infrequently bought commodity, consumers would publish the realemotion online relatively, so the key issue here is to identify consumers’ emotioninclination, it has the particularly important practical application value in theautomotive field.This paper is aiming at the classification result of each sentimental classifier onthe online reviews of automobile field, finding out the best fit classification, andfinally building and supporting a sentiment mining of Auto reviews system. At first,this article analyzes the reseach background and current situation of the textsentiment classification, and sums up three mainly models, Naive Bayes, SVM,decision tree to classify our data. In order to balance pros and cons of these threemethods, in our experiment, our paper divides the automobiles’ online reviews intotwo part, structured reviews and unstructured reviews. So we fetch the data fromtwo different sources, aggregate and transform the data, and then import models,check and compare the relative index.So this research application can efficiently obtain the positve and negativeevaluation of the traits on the product or service, this can assist decision effectively.In the technical guidance, this paper gives quantitative analysis on the textcategorization model based on machine learning, sets up a full-scale automobilesentiment dictionary; in the behavior orientation, this research discusses deeply inthe sentimentic classification of online reviews in the auto industry, it possessesvery important value in theory and practice, it gives a guidance on the building-upof integrated online market data mining system.