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Identification Model of Commodity False Reviews Based on Integrated Features

机译:基于集成特征的商品虚假评论识别模型

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More and more consumers like online shopping, and false reviews of goods mislead consumers to some extent, so it is necessary to design a false review detection method. Based on the integration characteristics of consumer history reviews, this paper proposes an online false comment detection model. The model introduces time series and combines the static and dynamic features of the reviews to detect false comments. Finally, the model is used to test the comment data of three kinds of Amazon products. The results show that the model has high recognition accuracy.
机译:越来越多的消费者喜欢在线购物,对商品的虚假评论在某种程度上误导了消费者,因此有必要设计一种虚假评论检测方法。基于消费者历史评论的整合特征,提出了一种在线虚假评论检测模型。该模型引入了时间序列,并结合了评论的静态和动态特征以检测错误的评论。最后,该模型用于测试三种亚马逊产品的评论数据。结果表明,该模型具有较高的识别精度。

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