Product Aspect Clustering by Incorporating Background Knowledge for Opinion Mining.
Product Aspect Clustering by Incorporating Background Knowledge for Opinion Mining.
复制标题
通过结合意见挖掘的背景知识进行产品方面聚类
DOI：
10.1371/journal.pone.0159901
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发表时间：
2016
期刊：
影响因子：
3.7
通讯作者：
Liu T
中科院分区：
文献类型：
--
作者：
Chen Y;Zhao Y;Qin B;Liu T
产品外观识别是细粒度意见挖掘中的一项关键任务。目前的方法主要集中在从产品评论中提取方面。然而，将同义提取的方面聚类到同一类别中也很重要。在本文中，我们专注于产品方面聚类问题。主要的挑战是正确地聚类和概括具有相似含义但不同表示的方面。为了解决这个问题，我们学习两种类型的背景知识，为每个提取的方面的基础上两种类型的有效方面的关系：相关方面的关系和不相关的方面的关系，这描述了两个方面之间的两种不同类型的关系。基于这两种关系，我们可以将许多相关和不相关的方面分配到两个不同的集合中，作为背景知识来描述每个产品方面。为了获得每个产品方面的丰富的背景知识，我们可以从Web上丰富的背景知识的可用信息。然后，我们设计了一个层次聚类算法聚类这些方面到不同的群体，其中方面相似性计算使用的相关和不相关的方面集为每个产品方面。在相机和移动的手机领域获得的实验结果表明，所提出的产品方面聚类方法的基础上的两种类型的背景知识比基线的方法，而不使用的背景知识的表现更好。此外，实验结果还表明，利用Web扩展可用的背景知识是可行的。
Product aspect recognition is a key task in fine-grained opinion mining. Current methods primarily focus on the extraction of aspects from the product reviews. However, it is also important to cluster synonymous extracted aspects into the same category. In this paper, we focus on the problem of product aspect clustering. The primary challenge is to properly cluster and generalize aspects that have similar meanings but different representations. To address this problem, we learn two types of background knowledge for each extracted aspect based on two types of effective aspect relations: relevant aspect relations and irrelevant aspect relations, which describe two different types of relationships between two aspects. Based on these two types of relationships, we can assign many relevant and irrelevant aspects into two different sets as the background knowledge to describe each product aspect. To obtain abundant background knowledge for each product aspect, we can enrich the available information with background knowledge from the Web. Then, we design a hierarchical clustering algorithm to cluster these aspects into different groups, in which aspect similarity is computed using the relevant and irrelevant aspect sets for each product aspect. Experimental results obtained in both camera and mobile phone domains demonstrate that the proposed product aspect clustering method based on two types of background knowledge performs better than the baseline approach without the use of background knowledge. Moreover, the experimental results also indicate that expanding the available background knowledge using the Web is feasible.