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Research on comment target extracting in Chinese online shopping platform

机译：中文网购平台评论目标提取研究

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Purpose This paper aims to extract the comment targets in Chinese online shopping platform. Design/methodology/approach The authors first collect the comment texts, word segmentation, part-of-speech (POS) tagging and extracted feature words twice. Then they cluster the evaluation sentence and find the association rules between the evaluation words and the evaluation object. At the same time, they establish the association rule table. Finally, the authors can mine the evaluation object of comment sentence according to the evaluation word and the association rule table. At last, they obtain comment data from Taobao and demonstrate that the method proposed in this paper is effective by experiment. Findings The extracting comment target method the authors proposed in this paper is effective. Research limitations/implications First, the study object of extracting implicit features is review clauses, and not considering the context information, which may affect the accuracy of the feature excavation to a certain degree. Second, when extracting feature words, the low-frequency feature words are not considered, but some low-frequency feature words also contain effective information. Practical implications Because of the mass online reviews data, reading every comment one by one is impossible. Therefore, it is important that research on handling product comments and present useful or interest comments for clients. Originality/value The extracting comment target method the authors proposed in this paper is effective.
机译：目的本文旨在提取中文在线购物平台中的评论目标。设计/方法/方法作者首先收集注释文本，分词，词性（POS）标记，然后提取特征词两次。然后他们对评估语句进行聚类，并找到评估词与评估对象之间的关联规则。同时，他们建立关联规则表。最后，作者可以根据评价词和关联规则表挖掘评论句的评价对象。最后，他们从淘宝网获得评论数据，并通过实验证明了本文提出的方法是有效的。结论本文提出的提取评论目标方法是有效的。研究的局限性/意义首先，提取隐含特征的研究对象是评论子句，而不考虑上下文信息，这可能会在一定程度上影响特征挖掘的准确性。其次，在提取特征词时，不考虑低频特征词，但是一些低频特征词也包含有效信息。实际意义由于在线评论的大量数据，不可能一一阅读每个评论。因此，研究处理产品评论并为客户提供有用或感兴趣的评论非常重要。原创性/价值作者提出的提取评论目标方法是有效的。

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