论文标题
使用深度学习的垃圾邮件审查检测
Spam Review Detection Using Deep Learning
论文作者
论文摘要
在当今世界上，一个强大而可靠的检测垃圾邮件评论的系统是哭泣的需求，以便购买产品而不会被在线网站欺骗。在许多在线站点中，有一些发布评论的选项，因此为假付费评论或不正确的评论创建了范围。这些炮制的评论可能会误导公众，并使他们感到困惑，无论是否相信评论。已经引入了重要的机器学习技术来解决垃圾邮件审查检测的问题。当前的大多数研究都集中在监督的学习方法上，这些方法需要标记的数据 - 在线审查时不足。我们在本文中的重点是检测任何欺骗性的文本评论。为了实现这一目标，我们已经与标记和未标记的数据和拟议的垃圾邮件审查检测提出的深度学习方法合作，其中包括多层感知器（MLP）（MLP），卷积神经网络（CNN）和漫长的短期记忆（LSTM）的复发性神经网络（RNN）的变体。我们还应用了一些传统的机器学习分类器，例如中殿贝叶斯（NB），K最近的邻居（KNN）和支持向量机（SVM）来检测垃圾邮件评论，最后，我们显示了传统和深度学习分类器的性能比较。
A robust and reliable system of detecting spam reviews is a crying need in todays world in order to purchase products without being cheated from online sites. In many online sites, there are options for posting reviews, and thus creating scopes for fake paid reviews or untruthful reviews. These concocted reviews can mislead the general public and put them in a perplexity whether to believe the review or not. Prominent machine learning techniques have been introduced to solve the problem of spam review detection. The majority of current research has concentrated on supervised learning methods, which require labeled data - an inadequacy when it comes to online review. Our focus in this article is to detect any deceptive text reviews. In order to achieve that we have worked with both labeled and unlabeled data and proposed deep learning methods for spam review detection which includes Multi-Layer Perceptron (MLP), Convolutional Neural Network (CNN) and a variant of Recurrent Neural Network (RNN) that is Long Short-Term Memory (LSTM). We have also applied some traditional machine learning classifiers such as Nave Bayes (NB), K Nearest Neighbor (KNN) and Support Vector Machine (SVM) to detect spam reviews and finally, we have shown the performance comparison for both traditional and deep learning classifiers.
