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Received:October 26, 2012 Revised:November 26, 2012
Received:October 26, 2012 Revised:November 26, 2012
中文摘要: 在用户评论中蕴含了大量的产品特征和用户对这些特征的观点和态度. 本研究提出了基于Apriori关联规则算法的产品特征抽取方法, 利用与种子特征集合的互信息和与观点词的共现度对候选特征进行过滤; 并提出了一种特征自动聚类方法, 以特征词间的字符串相似度和语义相似度以及特征所对应的观点词作为衡量产品特征之间关联程度的特征, 采用K-means聚类算法对产品特征进行聚类. 本研究采用大众点评网对美食店铺的评论语料, 对该方法进行了数据实验,实验结果初步验证了该方法有效性.
Abstract:User Reviews contains a large number of product features and user's opinions towards these features. This paper proposed an approach to extract product features, which is based on Apriori algorithm, and using PMI with the seed set and co-occurrence degree with opinion words to filter features. And then an approach to group product features based on K-means algorithm is proposed, in which sharing words, lexical similarity and opinion words are chosen as the tokens to represent the association of product features. With the Chinese reviews of restaurants from the Internet, experimental results demonstrate the validity of the proposed method．
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基金项目:国家自然科学基金(61072057,61101119,61121001,60902051);长江学者和创新团队发展计划(IRT1049);国家科技重大专项(2011ZX03002-001-01)
引用文本：
韩雪婷,李炜,沈奇威.用户评论中产品特征的抽取及聚类.计算机系统应用,2013,22(5):188-192
HAN Xue-Ting,LI Wei,SHEN Qi-Wei.Extracting and Clustering Product Features from User Reviews.COMPUTER SYSTEMS APPLICATIONS,2013,22(5):188-192
韩雪婷,李炜,沈奇威.用户评论中产品特征的抽取及聚类.计算机系统应用,2013,22(5):188-192
HAN Xue-Ting,LI Wei,SHEN Qi-Wei.Extracting and Clustering Product Features from User Reviews.COMPUTER SYSTEMS APPLICATIONS,2013,22(5):188-192
