Research On The Quality Of Online Book Reviews Based On Text Analysis

With the arrival of big data's era.more and more people share their views and ideas through the Internet.Online comments of users are exponentially explosive.The control and utilization of comments has become an important test faced by current network platforms.A good network comment management system should have two functions,one is to help users get useful information from massive data quickly,the other is to help the platform manage and utilize user comments reasonably and effectively.As a branch of natural language processing,comment quality assessment has become an important part of network comment management system.The purpose of comment quality assessment is to quantify the quality of comments through some measurable indicators or to classify comments according to their quality and identify high-quality comments.On this basis,comments are filtered,sorted,so as to help readers obtain valuable comment information quickly.On the one hand,the review quality evaluation of non-commercial book platform can help readers quickly and efficiently find valuable reviews in the massive review information and help them select more suitable and high-quality books.On the other hand,it can improve the existing review display function of the book portal,improve the service quality of the website and improve the user experience.This paper studies the quality assessment of user reviews on non-commercial book platforms.Firstly,according to the characteristics of non-commercial book platforms and Chinese language,a set of online comment quality evaluation index system--WDC comment quality evaluation index system suitable for this type of platform is constructed.Then,based on this index,the feasibility of classification by SVM method and logistic regression method is analyzed.In the end,with"douban reading"site three types of book reviews data has carried on the empirical analysis,support vector machine(SVM)method and logistic regression were used respectively to establish the online reviews quality evaluation model.To analyze the classification effect,precision rate,recall rate,F value,and accuracy rate are used.In this evaluation system,we find the effect of SVM classification method is superior to logistic regression method.At the same time,the random forest method was adopted to sort the indicators and the conclusion was drawn:for the user comments on the non-commercial book platform.the number of modifiers in the comments had the largest impact on the comment quality,followed by the average sentence length and the number of characters,and the difference in the score had the least impact on the comment quality.The innovation of this study is to construct a comment quality evaluation system for non-commercial platforms with less research before.And when labeling the quality of the training set,the combination of useful voting and manual labeling is adopted.Furthermore,in the past,the longer the comment is,we will consider it more useful,but in this label the moderate data will be judged useful.This improvement enriches the definition of usefulness.The research results of this paper enrich the research content of the online evaluation quality evaluation of non-commercial platform,which has laid a foundation for the follow-up research.