【摘要】 评论是一种反映事物价值的重要主观信息。该文从用户角度出发,提出一种基于全局用户意图的商品评论自动估价方法。该研究首先定义了一种简易的评论价值划分标准("实用"和"垃圾"评论),借以实现前瞻性的方法尝试。在此基础上,该文采用SVM分类器作为划分评论价值类别(二元分类问题)的基本平台,并基于这一平台重点考察三种影响评论价值的特征:1)属性热度;2)内容可信度;3)用户情感和观点。该文在文本结构特征的基础上,加入上述三类反映用户意图的特征进行评论价值判定,并在大规模商品评论语料集中进行测试。实验表明通过引入用户意图特征,评论自动估价的性能有较大幅度提高。更多还原

【Abstract】 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.更多还原