Abstract
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.
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Zhou, Q., Zhang, Y., Hu, X. (2012). An Ensemble Method Based on Confidence Probability for Multi-domain Sentiment Classification. In: Huang, DS., Jiang, C., Bevilacqua, V., Figueroa, J.C. (eds) Intelligent Computing Technology. ICIC 2012. Lecture Notes in Computer Science, vol 7389. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-31588-6_28
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DOI: https://doi.org/10.1007/978-3-642-31588-6_28
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