Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/28682
Title: Multi-domain sentiment classification with classifier combination
Authors: Li, SS
Huang, CR 
Zong, CQ
Keywords: Multi-domain learning
Multiple classifier system
Sentiment classification
Issue Date: 2010
Source: Journal of computer science and technology, 2010, v. 26, no. 1, p. 25-33 How to cite?
Journal: Journal of Computer Science and Technology 
Abstract: State-of-the-arts studies on sentiment classification are typically domain-dependent and domain-restricted. In this paper, we aim to reduce domain dependency and improve overall performance simultaneously by proposing an efficient multi-domain sentiment classification algorithm. Our method employs the approach of multiple classifier combination. In this approach, we first train single domain classifiers separately with domain specific data, and then combine the classifiers for the final decision. Our experiments show that this approach performs much better than both single domain classification approach (using the training data individually) and mixed domain classification approach (simply combining all the training data). In particular, classifier combination with weighted sum rule obtains an average error reduction of 27.6% over single domain classification.
URI: http://hdl.handle.net/10397/28682
ISSN: 1000-9000
DOI: 10.1007/s11390-011-9412-y
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