Semi-Supervised Learning for Imbalanced Sentiment Classification
Proceedings of the Twenty-Second International Joint Conference on Artificial Intelligence. Pages 1826-1830.
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.
@inproceedings{ijcai2011p306,
title = {Semi-Supervised Learning for Imbalanced Sentiment Classification},
author = {Li, Shoushan and Wang, Zhongqing and Zhou, Guodong and Lee, Sophia Yat Mei},
booktitle = {Proceedings of the Twenty-Second International Joint Conference on
Artificial Intelligence, {IJCAI-11}},
publisher = {AAAI Press/International Joint Conferences on Artificial Intelligence},
editor = {Toby Walsh},
pages = {1826--1830},
year = {2011},
month = {7},
doi = {10.5591/978-1-57735-516-8/IJCAI11-306},
url = {https://doi.org/10.5591/978-1-57735-516-8/IJCAI11-306},
}
TY - CPAPER TI - Semi-Supervised Learning for Imbalanced Sentiment Classification AU - Li, Shoushan AU - Wang, Zhongqing AU - Zhou, Guodong AU - Lee, Sophia Yat Mei T2 - Proceedings of the Twenty-Second International Joint Conference on Artificial Intelligence, IJCAI-11 PB - AAAI Press/International Joint Conferences on Artificial Intelligence PY - 2011 SP - 1826 EP - 1830 DO - 10.5591/978-1-57735-516-8/IJCAI11-306 UR - https://doi.org/10.5591/978-1-57735-516-8/IJCAI11-306 ER -Copied
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