基于全局用户意图的评论自动估价方法研究

Automatic Reviews Quality Evaluation Based on Global User Intent

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。

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 di- vide 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 informa- tion of product, reviewers＇ opinion and review credibility. In this paper, we combine text structure feature with a- bove three kinds of features which reflect the global user intent, and then test on a large-scale corpus of product re- views. The experimental results show a significant improvement on the global accuracy by involving diverse user in- tent features.