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- W5236451 abstract "Various semi-supervised learning methods have been proposed recently to solve the long-standing shortage problem of manually labeled data in sentiment classification. However, most existing studies assume the balance between negative and positive samples in both the labeled and unlabeled data, which may not be true in reality. In this paper, we investigate a more common case of semi-supervised learning for imbalanced sentiment classification. In particular, various random subspaces are dynamically generated to deal with the imbalanced class distribution problem. Evaluation across four domains shows the effectiveness of our approach." @default.
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- W5236451 date "2011-07-16" @default.
- W5236451 modified "2023-10-02" @default.
- W5236451 title "Semi-supervised learning for imbalanced sentiment classification" @default.
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- W5236451 doi "https://doi.org/10.5591/978-1-57735-516-8/ijcai11-306" @default.
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