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One of the most widely-studied sub-problems of opinion mining is sentiment classification, which classifies evaluative texts as positive or negative to help people automatically identify the viewpoints underlying the online user-generated information. Most of the existing methods for sentiment classification ignore word sequence and unlabeled test documents' structural information. This paper proposes...
Web content mining is intended to help people to discover valuable information from large amount of unstructured data on the Web. Sentiment classification aims to mining the Web content of product reviews by classifying the reviews into positive or negative opinions. Such kind of classification approaches could help both consumers and sellers in making their decisions. But it is also a complicated...
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