论文标题
通过合奏学习的假评论检测
Fake Reviews Detection through Ensemble Learning
论文作者
论文摘要
客户通过利用在线评论来分享他们的经验来代表他们对消费产品的满意。几种基于机器学习的方法可以自动检测欺骗性和虚假评论。最近，有一些研究报告了与传统的机器学习技术相比，基于集合学习的方法的性能。在整体学习的最新趋势中，本文评估了基于集合学习的方法的性能，以识别虚假的在线信息。我们开发的许多基于整体学习的方法在一系列假餐厅评论中的应用表明，这些基于整体学习的方法比传统的机器学习算法更好地检测欺骗性信息。
Customers represent their satisfactions of consuming products by sharing their experiences through the utilization of online reviews. Several machine learning-based approaches can automatically detect deceptive and fake reviews. Recently, there have been studies reporting the performance of ensemble learning-based approaches in comparison to conventional machine learning techniques. Motivated by the recent trends in ensemble learning, this paper evaluates the performance of ensemble learning-based approaches to identify bogus online information. The application of a number of ensemble learning-based approaches to a collection of fake restaurant reviews that we developed show that these ensemble learning-based approaches detect deceptive information better than conventional machine learning algorithms.
