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首页> 外文期刊>Procedia Computer Science >Deceptive Opinions Detection Using New Proposed Arabic Semantic Features
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Deceptive Opinions Detection Using New Proposed Arabic Semantic Features

机译：使用新的阿拉伯语语义特征检测欺骗性意见

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Some users try to post false reviews to promote or to devalue other’s products and services. This action is known as deceptive opinions spam, where spammers try to gain or to profit from posting untruthful reviews. Therefore, we conducted this work to develop and to implement new semantic features to improve the Arabic deception detection. These features were inspired from the study of discourse parse and the rhetoric relations in Arabic. Looking to the importance of the phrase unit in the Arabic language and the grammatical studies, we have analyzed and selected the most used unit markers and relations to calculate the proposed features. These last were used basically to represent the reviews texts in the classification phase. Thus, the most accurate classification technique used in this area which has been proven by several previous works is the Support Vector Machine classifier (SVM). But there is always a lack concerning the Arabic annotated resources specially for deception detection area as it is considered new research area. Therefore, we used the semi supervised SVM to overcome this problem by using the unlabeled data.
机译：有些用户试图发布虚假审查以推广或贬值其他的产品和服务。这种行动被称为欺骗性意见垃圾邮件，垃圾邮件发送者试图获得或从发布不诚实的评论中获益。因此，我们开展了这项工作，开发并实施新的语义特征，以提高阿拉伯欺骗性检测。这些特征是从语话语解析的研究和阿拉伯语中的修辞关系的启发。在阿拉伯语和语法研究中展望了短语单元的重要性，我们已经分析并选择了最常用的单位标记和关系来计算所提出的功能。这些最后用于代表分类阶段中的评论文本。因此，通过若干以前的作品被证明的该区域中使用的最准确的分类技术是支持向量机分类器（SVM）。但总是缺乏特别是欺骗检测区域的阿拉伯语注释资源，因为它被认为是新的研究区域。因此，我们使用SEMI监督SVM来克服未标记的数据来克服这个问题。

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