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Product review management software based on multiple classifiers

机译：基于多个分类器的产品评论管理软件

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摘要

In recent years, due to significant developments in online shopping and the widespread use of e-commerce, competition among companies has increased considerably. As a result, product reviews have become a primary factor in consumers' decision making, which has given rise to a market for fraudulent reviews about real products and services. In this study, the authors propose a model using a multiple classifier system to identify deceptive negative customer reviews, which they validated with a dataset of hotel reviews from TripAdvisor. The proposed model used five classifiers by following the majority voting combination rule - namely, libLinear, libSVM, sequential minimal optimisation, random forest, and J48 - the first two of which represent different implementations of support vector machines. Ultimately, the model provided remarkable results that demonstrate improvement upon approaches reported in the literature.
机译：近年来，由于在线购物的显着发展和电子商务的广泛使用，公司之间的竞争已大大增加。结果，产品评论已成为消费者决策的主要因素，这引起了对真实产品和服务的欺诈性评论的市场。在这项研究中，作者提出了一个使用多重分类器系统的模型，以识别欺骗性的负面顾客评论，并用TripAdvisor的酒店评论数据集进行了验证。提出的模型通过遵循多数投票组合规则使用了五个分类器-libLinear，libSVM，顺序最小优化，随机森林和J48-其中前两个代表支持向量机的不同实现。最终，该模型提供了非凡的结果，证明了对文献报道方法的改进。

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