Difference between revisions of "Li et al IJCAI 11"
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Revision as of 02:58, 27 September 2012
This a Paper that appeared at the International joint conference on Artificial Intelligence 2011
Citation
Incorporating reviewer and product information for review rating prediction Li, F. and Liu, N. and Jin, H. and Zhao, K. and Yang, Q. and Zhu, X. Proceedings of the Twenty-Second international joint conference on Artificial Intelligence-Volume Volume Three pages 1820--1825 year 2011
Online version
Incorporating reviewer and product information for review rating prediction
Summary
This Paper is using Tensor Analysis in order to do Review classification in cases where a word in a review might have different sentiment coloring, depending on the particular reviewer. To that end, the authors model the problem as a three dimensional Tensor, where the three dimensions dimensions correspond to a reviewer, a product and a term respectively, thus incorporating additional information, about which specific reviewer wrote what, to the traditional Bag of Words model.
Another point to note is that the authors are not doing Binary Classification to the reviews, but wish to rate each review on a scale from 1-5 (similar to what Amazon does for example). In order to come up with a rating scheme, the authors use a Linear Regression function. Based on that function, they derive a decomposition of the reviewer-by-product-by-term tensor into three, compact factor matrices (each of which corresponds to the respective dimension), and using those matrices, they are able to infer the missing rating scores.
Evaluation
For evaluation, the authors conduct experiments on two different, real datasets:
- Movie dataset, downloaded from http://www.cs.cornell.edu/people/pabo/movie-review-data/
- Epinions dataset, crawled by the authors.
A description of the data is shown in the table below:
Results:
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Study Plan
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