acl acl2013 acl2013-131 acl2013-131-reference knowledge-graph by maker-knowledge-mining
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Author: Rui Xia ; Tao Wang ; Xuelei Hu ; Shoushan Li ; Chengqing Zong
Abstract: Bag-of-words (BOW) is now the most popular way to model text in machine learning based sentiment classification. However, the performance of such approach sometimes remains rather limited due to some fundamental deficiencies of the BOW model. In this paper, we focus on the polarity shift problem, and propose a novel approach, called dual training and dual prediction (DTDP), to address it. The basic idea of DTDP is to first generate artificial samples that are polarity-opposite to the original samples by polarity reversion, and then leverage both the original and opposite samples for (dual) training and (dual) prediction. Experimental results on four datasets demonstrate the effectiveness of the proposed approach for polarity classification. 1
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