Combining Review Text Content and Reviewer-Item Rating Matrix to Predict Review Rating
Joint Authors
Huang, Y.
Wang, Bingkun
Li, Xing
Source
Computational Intelligence and Neuroscience
Issue
Vol. 2016, Issue 2016 (31 Dec. 2015), pp.1-11, 11 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2016-01-03
Country of Publication
Egypt
No. of Pages
11
Main Subjects
Abstract EN
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.
American Psychological Association (APA)
Wang, Bingkun& Huang, Y.& Li, Xing. 2016. Combining Review Text Content and Reviewer-Item Rating Matrix to Predict Review Rating. Computational Intelligence and Neuroscience،Vol. 2016, no. 2016, pp.1-11.
https://search.emarefa.net/detail/BIM-1099713
Modern Language Association (MLA)
Wang, Bingkun…[et al.]. Combining Review Text Content and Reviewer-Item Rating Matrix to Predict Review Rating. Computational Intelligence and Neuroscience Vol. 2016, no. 2016 (2015), pp.1-11.
https://search.emarefa.net/detail/BIM-1099713
American Medical Association (AMA)
Wang, Bingkun& Huang, Y.& Li, Xing. Combining Review Text Content and Reviewer-Item Rating Matrix to Predict Review Rating. Computational Intelligence and Neuroscience. 2016. Vol. 2016, no. 2016, pp.1-11.
https://search.emarefa.net/detail/BIM-1099713
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1099713