该文主要把产品评论中属性的不同描述进行归类.在产品评论中,同类的属性会有不同的描述,例如,手机的“外形”和“设计”指的是同类属性.同类属性虽然有不同的描述,但是在句中却和相同的情感词搭配使用.该文首先抽取评论句中属性和情感词的搭配关系,形成一个二部图,然后用权重标准化SimRank计算不同属性之间的相似度,并把所得的结果与半监督学习中的贝叶斯分类器进行融合,得到了更好的分类结果.通过实验证明了此方法的有效性.%This paper focuses on clustering different feature expressions in product reviews into proper groups. In product reviews, the same features may have different expressions, e. G. "appearance" and "design" of a mobile phone actuallyindicate the same feature. Considering the fact that different expressions are always used with same sentimental words in a sentence, this paper first extracts product feature expressions and sentimental words in pairs to build a bipartite graph, and then adopts the Weight Normalized SimRank to compute similarity between different feature expressions in the bipartite graph, and finally optimizes the Bayesian classifier in Semi-Supervised Learning via the similarity. Experimental results show that the proposed method is valid.
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