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.
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Supported by the National Natural Science Foundation of China under Grant No. 61003155 and Start-Up Grant for Newly Appointed Professors under Grant No. 1-BBZM in The Hong Kong Polytechnic University.
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Li, SS., Huang, CR. & Zong, CQ. Multi-Domain Sentiment Classification with Classifier Combination. J. Comput. Sci. Technol. 26, 25–33 (2011). https://doi.org/10.1007/s11390-011-9412-y
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DOI: https://doi.org/10.1007/s11390-011-9412-y