《计算机应用研究》|Application Research of Computers

基于改进SimRank的产品特征聚类研究

Product feature clustering based on improved SimRank

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作者 刘臣,段俊
机构 上海理工大学 管理学院,上海 200093
统计 摘要被查看 次,已被下载
文章编号 1001-3695(2019)07-007-1951-04
DOI 10.19734/j.issn.1001-3695.2018.01.0027
摘要 针对在线用户评论中产品特征的提取和聚类问题进行了研究,提出一种改进的SimRank算法。将情感词—特征对放入二分网中,在二分网中使用改进后的SimRank算法计算特征词之间的相似度;再通过谱聚类算法对特征相似度进行聚类,提取网络产品的特征集合。以某电脑评论为例,从中提取情感词—特征对进行研究。实验结果显示,改进后的算法准确率更高。改进后的特征相似度检测方法可以作为检测特征相似度的有效方法,实验采用在线产品的评论语料。实验结果表明,使用改进后的SimRank相似度对特征词进行聚类提取出特征更加准确。
关键词 SimRank算法; 特征聚类; 二分网; 特征相似度
基金项目 国家自然科学基金资助项目(71401107,71774111)
本文URL http://www.arocmag.com/article/01-2019-07-007.html
英文标题 Product feature clustering based on improved SimRank
作者英文名 Liu Chen, Duan Jun
机构英文名 Business School,University of Shanghai for Science & Technology,Shanghai 200093,China
英文摘要 This paper studied the extraction and clustering of product features in online user reviews. It proposed an improved SimRank algorithm to put the affective word-feature pair into the binary network. And it used the improved SimRank algorithm to compute the similarity between the characteristic words. Then it adopted the spectral clustering algorithm to cluster the feature similarity. Extracts feature sets for network products. Taking a computer commentary as an example, this paper extracted affective word-feature pairs. The experimental results show that the improved algorithm has higher accuracy. The improved feature similarity detection method can be used as an effective method for detecting feature similarity. The experimental results show that using the improved SimRank similarity to extract the feature words is more accurate.
英文关键词 SimRank algorithm; feature clustering; binary network; feature similarity
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收稿日期 2018/1/17
修回日期 2018/3/8
页码 1951-1954
中图分类号 TP391
文献标志码 A