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DRI-RCNN: An approach to deceptive review identification using recurrent convolutional neural network

机译：DRI-RCNN：一种使用循环卷积神经网络进行欺骗性评论识别的方法

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摘要

With the widespread of deceptive opinions in the Internet, how to identify online deceptive reviews automatically has become an attractive topic in research field. Traditional methods concentrate on extracting different features from online reviews and training machine learning classifiers to produce models to decide whether an incoming review is deceptive or not. This paper proposes an approach called DRI-RCNN (Deceptive Review Identification by Recurrent Convolutional Neural Network) to identify deceptive reviews by using word contexts and deep learning. The basic idea is that since deceptive reviews and truthful reviews are written by writers without and with real experience respectively, the writers of the reviews should have different contextual knowledge on their target objectives under description. In order to differentiate the deceptive and truthful contextual knowledge embodied in the online reviews, we represent each word in a review with six components as a recurrent convolutional vector. The first and second components are two numerical word vectors derived from training deceptive and truthful reviews, respectively. The third and fourth components are left neighboring deceptive and truthful context vectors derived by training a recurrent convolutional neural network on context vectors and word vectors of left words. The fifth and six components are right neighboring deceptive and truthful context vectors of right words. Further, we employ max-pooling and ReLU (Rectified Linear Unit) filter to transfer recurrent convolutional vectors of words in a review to a review vector by extracting positive maximum feature elements in recurrent convolutional vectors of words in the review. Experiment results on the spam dataset and the deception dataset demonstrate that the proposed DRI-RCNN approach outperforms the state-of-the-art techniques in deceptive review identification.
机译：随着Internet上欺骗性观点的广泛普及，如何自动识别在线欺骗性评论已成为研究领域的一个热门话题。传统方法集中于从在线评论和训练机器学习分类器中提取不同的特征，以生成模型来确定传入的评论是否具有欺骗性。本文提出了一种称为DRI-RCNN（通过循环卷积神经网络进行欺骗性评论识别）的方法，以通过使用单词上下文和深度学习来识别欺骗性评论。基本思想是，由于欺骗性评论和真实性评论分别是由没有经验和有实际经验的作家撰写的，因此评论的作者对于所描述的目标应具有不同的上下文知识。为了区分在线评论中包含的欺骗性和真实的上下文知识，我们在评论中将每个单词表示为具有六个成分的循环卷积向量。第一部分和第二部分是分别从训练欺骗性评论和真实性评论得出的两个数字单词向量。第三和第四部分是左邻的欺骗性和真实的上下文向量，它们是通过在左词的上下文向量和词向量上训练循环卷积神经网络而得出的。第五和第六部分是正确的单词的右邻欺骗性和真实上下文向量。此外，我们通过提取评论中单词的经常卷积向量中的正最大特征元素，采用最大池和ReLU（整流线性单元）滤波器将评论中单词的循环卷积向量转移到评论向量。在垃圾邮件数据集和欺骗数据集上的实验结果表明，所提出的DRI-RCNN方法在欺骗性评论识别中的性能优于最新技术。

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