The Analysis Of Online Commentary Usefulness Based On Text Mining

With the development of the Internet,in the tense of "Internet?",China's film industry has begun to enter the development model of "Internet ? movie",this model developed to the present,not only to promote people to consume in the film industry,also led to the rapid development of other entertainment industry.Coupled with the popularity of various social shopping platforms for mobile terminal,people rely more on these platforms to satisfy their own needs.Now China's film industry also ushered in the rapid rise of the stage,but how the Internet in the mass of film information in the closest screening of the audience the ideal information,that needs to pay special attention to the perceived value of the audience.First,this paper reviews the predecessors' study on online reviews,and draws lessons from them on the basis of previous studies and new ideas.Based on the Web crawler technology and text mining technology,this paper analyzes the factors that affect the usefulness of online critics by combining theoretical research with empirical analysis.Based on the analysis of the various dimensions of the Douban movie network critic data,establish a useful review ballot votes rate as the dependent variable,the total number of votes,emotional film critic,film critic,film star published text length,the degree of concern,the critics released the total number of critics,reviewers add bean time,comment text for regression effect model of factors.Using R software to analyze and establish the model,to quantitatively describe the influencing factors of the usefulness of the film critics,and to draw the conclusion of this paper,and it is of great significance for the online users to find the critics quickly and effectively.