Abstract：Cross-domain sentiment classification has attracted more attention in natural language processing field currently. It aims to predict the text polarity of target domain with the help of labeled texts in source domain. Usually,traditional supervised classification approaches can not perform well due to the difference of data distribution between domains. In this paper,a weighted SimRank algorithm is proposed to address this problem. The weighted SimRank algorithm is applied to construct a Latent Feature Space (LFS) with feature similarity. Then each sample is reweighted by the mapping function learned from the LFS. After reducing the mismatch of data distribution between domains,the algorithm performs well on cross-domain sentiment classification. The experiment verifies the effectiveness of the proposed algorithm.
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2013, Vol. 26 
