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Supervised sentiment classification approaches require labeled training (source) and testing (target) dataset. Generation of such datasets demands substantial time and effort but cross-domain classification minimizes the effort by considering two different domains for source and target datasets. In this paper, we propose Cross-D-Vectorizers i.e., a set of three sentiment n-gram feature-spaces (Lexical-TFIDF...
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