Abstract：This paper focuses on the opinion analysis of documents, i.e. to determine the overall opinion (e.g., negative or positive) of a given document. Existing studies have shown that, the supervised classification approaches usually perform well in this task. However, in most cases, the performance decreases sharply when the model is transferred from the labeled data domain to a different target domain without labeled data. This raises the issue of cross-domain opinion analysis. In this paper, we propose an iterative algorithm which integrated the opinion orientations of the documents into the graph-ranking algorithm for cross-domain opinion analysis. We apply the graph-ranking algorithm using the accurate labels of old-domain documents as well as the “pseudo” labels of new-domain documents. Over the results of the iterative algorithm, we try to further improve the performance by choosing the test documents whose opinions have been determined more accurately as “seeds”, and applying the EM algorithm again for cross-domain opinion analysis. The experiment results indicate that the proposed algorithm could improve the performance of cross-domain opinion analysis dramatically. Key wordscomputer application; Chinese information processing; cross domain; opinion analysis; graph ranking; EM algorithm
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2010, Vol. 24 
