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A novel context-based implicit feature extracting method

机译:一种新的基于上下文的隐式特征提取方法

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One of the major steps for opinion mining is to extract product features. The vast majority of existing approaches focus on explicit feature identification, few attempts have been made to identify implicit features in reviews, however; people tend to express their opinions with simple structures and brachylogies, which lead to more implicit features in reviews. By analyzing the characteristics of product reviews in Chinese on the Internet, this paper proposes a novel context-based implicit feature extracting method. We extract the implicit features according to the opinion words and the similarity between the product features in the implicit features' context. We also build a matrix to show the relationship between opinion words and product features, then use a new algorithm to filter the noises in the matrix. Experiments show that our method provides higher accuracy in extracting the implicit features.
机译:意见挖掘的主要步骤之一是提取产品功能。现有方法中的绝大多数集中在显式特征识别上,但是很少尝试在评论中识别隐式特征。人们倾向于通过简单的结构和短篇小说来表达自己的观点,从而导致评论具有更多隐含的特征。通过分析中文中文产品评论的特点,提出了一种基于上下文的隐式特征提取方法。我们根据意见词和隐含特征上下文中产品特征之间的相似性提取隐含特征。我们还建立了一个矩阵来显示意见词与产品特征之间的关系,然后使用一种新的算法来过滤矩阵中的噪声。实验表明,该方法在提取隐含特征方面具有较高的准确性。

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