基于实体关联网络模型的意见挖掘扩展

Keun-hyung Kim
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引用次数: 0

摘要

意见挖掘概括为在海量的在线客户评论中,对客户对产品或服务属性的敏感意见进行正面和负面的分类。由于客户通过主观意见和客观事实来代表他们的利益,因此现有的只能分析敏感意见的意见挖掘技术需要扩展。在本文中,我们提出了一种新的实体关联网络模型,它扩展了现有的意见挖掘技术。实体关联模型不仅可以表征敏感意见的积极程度和消极程度,还可以表征实体之间的关联程度和相对重要性。设计并实现了基于实体关联网络模型的客户评论分析系统。我们认识到,与现有的意见挖掘技术相比,该系统可以代表更丰富的信息。
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Expansion of Opinion Mining based on Entity Association Network Model
Opinion Mining summarizes with classifying sensitive opinions of customers in huge online customer reviews for the attributes of products or services by positive and negative opinions. Because the customers represent their interests through subjective opinions as well as objective facts, the existing opinion mining techniques, which can analyze just the sensitive opinions, need to be expanded.. In this paper, We propose the novel entity association network model which expands the existing opinion mining techniques. The entity association model can not only represent positive and negative degree of the sensitive opinions, but also can represent the degree of the associations and relative importances between entities. We designed and implemented the customer reviews analysis system based on the entity association network model. We recognized that the system can represent more abundant information than the existing opinion mining techniques.
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