Online product reviews play a major role in the success or failure of an E-commerce business. Before procuring products or services, the shoppers usually go through the online reviews posted by previous customers to get recommendations of the details of products and make purchasing decisions. Nevertheless, it is possible to enhance or hamper specific E-business products by posting fake reviews, which can be written by persons called fraudsters. These reviews can cause financial loss to E-commerce businesses and misguide consumers to take the wrong decision to search for alternative products. Thus, developing a fake review detection system is ultimately required for E-commerce business. The proposed methodology has used four standard fake review datasets of multidomains include hotels, restaurants, Yelp, and Amazon. Further, preprocessing methods such as stopword removal, punctuation removal, and tokenization have performed as well as padding sequence method for making the input sequence has fixed length during training, validation, and testing the model. As this methodology uses different sizes of datasets, various input word-embedding matrices of n-gram features of the review’s text are developed and created with help of word-embedding layer that is one component of the proposed model. Convolutional and max-pooling layers of the CNN technique are implemented for dimensionality reduction and feature extraction, respectively. Based on gate mechanisms, the LSTM layer is combined with the CNN technique for learning and handling the contextual information of n-gram features of the review’s text. Finally, a sigmoid activation function as the last layer of the proposed model receives the input sequences from the previous layer and performs binary classification task of review text into fake or truthful. In this paper, the proposed CNN-LSTM model was evaluated in two types of experiments, in-domain and cross-domain experiments. For an in-domain experiment, the model is applied on each dataset individually, while in the case of a cross-domain experiment, all datasets are gathered and put into a single data frame and evaluated entirely. The testing results of the model in-domain experiment datasets were 77%, 85%, 86%, and 87% in the terms of accuracy for restaurant, hotel, Yelp, and Amazon datasets, respectively. Concerning the cross-domain experiment, the proposed model has attained 89% accuracy. Furthermore, comparative analysis of the results of in-domain experiments with existing approaches has been done based on accuracy metric and, it is observed that the proposed model outperformed the compared methods.

中文翻译：

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利用多域数据集识别电子商务中虚假评论的集成神经网络模型的开发

在线产品评论在电子商务业务的成败中起着重要作用。在购买产品或服务之前，购物者通常会浏览以前客户发布的在线评论，以获取有关产品详细信息的建议并做出购买决定。但是，可以通过发布虚假评论来增强或阻碍特定的电子商务产品，这些评论可以由欺诈者撰写。这些评论可能会给电子商务业务造成财务损失，并误导消费者做出错误的决定来寻找替代产品。因此，电子商务业务最终需要开发伪造的评论检测系统。拟议的方法使用了四个标准的多域假审查数据集，包括旅馆，饭店，Yelp和亚马逊。更多，执行了诸如停用词删除，标点符号删除和标记化之类的预处理方法，以及用于在训练，验证和测试模型期间使输入序列具有固定长度的填充序列方法。由于此方法使用了不同大小的数据集，因此借助文字嵌入层（该模型的一个组成部分）来开发和创建评论文本的n元语法特征的各种输入文字嵌入矩阵。CNN技术的卷积层和最大合并层分别用于降维和特征提取。基于门机制，LSTM层与CNN技术相结合，用于学习和处理评论文本的n-gram特征的上下文信息。最后，作为拟议模型的最后一层，采用了S形激活函数，从上一层接收输入序列，并执行将评论文本分为伪造或真实的二元分类任务。在本文中，在两种类型的实验（域内和跨域实验）中对所提出的CNN-LSTM模型进行了评估。对于域内实验，将模型分别应用于每个数据集，而在跨域实验的情况下，将所有数据集收集并放入单个数据框中并进行完整评估。就餐厅，酒店，Yelp和Amazon数据集的准确性而言，模型域内实验数据集的测试结果分别为77％，85％，86％和87％。关于跨域实验，该模型已达到89％的准确性。此外，