Automatic Reviews Quality Evaluation Based on Global User Intent
Qiaoming Zhu · Zhongwen xinxi xuebao · 2012
Reviews reflect the value of things.From the customer's point of view,we propose a novel method for automatically evaluating the quality of product reviews based on the global-user-intent.In this paper,we firstly divide the reviews into two opposing groups,i.e.useful reviews and spammed reviews.By means of this definition,we attempt to realize a proactive approach.We experiment with SVM classifier to classify the quality of reviews.This is a typical binary classification and taking extra three kinds of features into consideration: the popular information of product,reviewers' opinion and review credibility.In this paper,we combine text structure feature with above three kinds of features which reflect the global user intent,and then test on a large-scale corpus of product reviews.The experimental results show a significant improvement on the global accuracy by involving diverse user intent features.