Multi-domain sentiment classification methods based on ensemble decision attracts more and more attention. These methods avoid collecting a large amount of new training data in target domain and expand aspect of deploying source domain systems. However, these methods face some important issues: the quantity of incorrect pre-labeled data remains high and the fixed weights limit accuracy of the ensemble classifier. Thus, we propose a novel method, named CEC, which integrates the ideas of self-training and co-training into multi-domain sentiment classification. Classification confidence is used to pre-label the data in the target domain. Meanwhile, CEC combines the base classifiers according to classification confidence probabilities when taking a vote for prediction. The experiments show the accuracy of the proposed algorithm has highly improved compared with the baseline algorithms. © 2012 Springer-Verlag.
CITATION STYLE
Zhou, Q., Zhang, Y., & Hu, X. (2012). An ensemble method based on confidence probability for multi-domain sentiment classification. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 7389 LNCS, pp. 214–220). https://doi.org/10.1007/978-3-642-31588-6_28
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