Research On Fake Review Identification Based On Text And User Behaviour Mining

Online reviews that were reviewed by consumers for a certain product or a store are usually one of the key factors,which determine consumers whether they would purchase product and service or not.As a result,some unscrupulous stores try to manipulate these online reviews out of their own interests.These activities result in a large number of fake reviews.These fake reviews have affected the interests of consumers and normal development of e-commerce.Therefore,detecting these fake reviews becomes an increasingly important task.In this thesis,a method based on text and user behavior mining was proposed to identify fake reviews.This thesis mainly completed the following tasks:(1)Extracted several effective fake reviews identification features.N-gram features,part-of-speech features,and LDA topic features were extracted by analyzing the text and semantics of reviews;maximum content similarity,positive review rate,maximum number of daily reviews,rating deviations,ratings,review text length,reviewer frequency and target item similarity were extracted by analyzing the user's anomalous behavior and potential relationships between review,reviewer and store.(2)Constructed a fake review detection model.Based on the extracted features,four types of indicators such as review textual feature index,review semantic feature index,user behavioral feature index,and relational feature index were constructed.Then a classification model was constructed based on SVM and XGboost classification algorithms.(3)Conducted an empirical analysis of the model.Five categories of experiments were designed based on review textual feature index,review semantic feature index,user behavioral feature index,relational feature index,and integrated feature index.In the end,the rationality of the selected features and the effectiveness of the construction model were demonstrated through the designed experiments.The research results provided some new ideas and effective identification methods for the study of fake review identification,and would offer a theoretical support in practice.