The purpose of this paper is automatic classification of TV series reviews based on generic categories.
What the authors mainly applied is using surrogate instead of specific roles or actors’ name in reviews to make reviews more generic. Besides, feature selection techniques and different kinds of classifiers are incorporated.
With roles’ and actors’ names replaced by generic tags, the experimental result showed that it can generalize well to agnostic TV series as compared with reviews keeping the original names.
The model presented in this paper must be built on top of an already existed knowledge base like Baidu Encyclopedia. Such database takes lots of work.
Like in digital information supply chain, if reviews are part of the information to be transported or exchanged, then the model presented in this paper can help automatically identify individual review according to different requirements and help the information sharing.
One originality is that the authors proposed the surrogate-based approach to make reviews more generic. Besides, they also built a review data set of hot Chinese TV series, which includes eight generic category labels for each review.
This study was partially supported by Natural Science Foundation of China Grant Number 71531013.
Ma, Y., Xia, L., Shen, W., Zhou, M. and Fan, W. (2017), "A surrogate-based generic classifier for Chinese TV series reviews", Information Discovery and Delivery, Vol. 45 No. 2, pp. 66-74. https://doi.org/10.1108/IDD-11-2016-0044
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