中文摘要：

动态情感知识的获取,特别是领域相关极性词典的构建一直是意见挖掘和情感分析系统在开放应用时面临的主要挑战之一。该文面向产品评价文本提出一种汉语情感极性词典扩展方法。该方法首先采用序列标注方法从意见文本中抽取产品意见要素,同时构建属性-评价对;然后,对抽取的属性-评价对进行正规化,以减少词典扩展中的复杂性和噪声;最后,改进PolarityRank算法的构图方式以使其适用于汉语文本,从而完成词典扩展。在汽车和手机两个领域的意见文本的实验结果表明领域相关的情感极性词语的扩展有利于情感极性分类性能的提高。

英文摘要：

In this paper we incorporate opinion element normalization with the PolarityRank algorithm and thus propose a semi-supervised approach to Chinese domain-specific sentiment lexicon expansion. We first extract a set of at- tribution-evaluation pairs from product reviews. In order to reduce the complexity and noises in sentiment lexicon expansion, we exploit Jaccard coefficient and rules to normalize the extracted product attributions and their relevant evaluations, respectively. Finally, we modify the PolarityRank algorithm to automatically recognize domain-specific dynamic polar words that are out of the original sentiment lexicon. Experimental results over product reviews in car and mobile-phone domains show that using the expanded domain-specific dynamic polar words helps improve polarity classification performance.