Combining Review Text Content and Reviewer-Item Rating Matrix to Predict Review Rating.
Combining Review Text Content and Reviewer-Item Rating Matrix to Predict Review Rating.
复制标题
结合评论文本内容和评论者-项目评分矩阵来预测评论评分
DOI：
10.1155/2016/5968705
复制
发表时间：
2016
影响因子：
--
通讯作者：
Li X
中科院分区：
文献类型：
--
作者：
Wang B;Huang Y;Li X
E-commerce develops rapidly. Learning and taking good advantage of the myriad reviews from online customers has become crucial to the success in this game, which calls for increasingly more accuracy in sentiment classification of these reviews. Therefore the finer-grained review rating prediction is preferred over the rough binary sentiment classification. There are mainly two types of method in current review rating prediction. One includes methods based on review text content which focus almost exclusively on textual content and seldom relate to those reviewers and items remarked in other relevant reviews. The other one contains methods based on collaborative filtering which extract information from previous records in the reviewer-item rating matrix, however, ignoring review textual content. Here we proposed a framework for review rating prediction which shows the effective combination of the two. Then we further proposed three specific methods under this framework. Experiments on two movie review datasets demonstrate that our review rating prediction framework has better performance than those previous methods.
