A Surrogate-based Generic Classifier for Chinese TV Series Reviews
Yufeng Ma, Long Xia, Wenqi Shen, Mi Zhou, Weiguo Fan
TL;DR
This paper presents a surrogate-based generic classifier for Chinese TV series reviews, enabling automatic categorization to assist viewers and producers, with promising results across multiple series.
Contribution
It introduces a novel surrogate-based approach for creating a general classifier applicable to various Chinese TV series reviews, improving classification effectiveness.
Findings
High classification accuracy demonstrated
Effective generalization across different TV series
Promising experimental performance
Abstract
With the emerging of various online video platforms like Youtube, Youku and LeTV, online TV series' reviews become more and more important both for viewers and producers. Customers rely heavily on these reviews before selecting TV series, while producers use them to improve the quality. As a result, automatically classifying reviews according to different requirements evolves as a popular research topic and is essential in our daily life. In this paper, we focused on reviews of hot TV series in China and successfully trained generic classifiers based on eight predefined categories. The experimental results showed promising performance and effectiveness of its generalization to different TV series.
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Taxonomy
TopicsSentiment Analysis and Opinion Mining · Topic Modeling · Text and Document Classification Technologies