TY -的A2 Algalil法赫德Abd盟——Alsubari萨利赫Nagi AU -德斯穆克萨钦n . AU - Al-Adhaileh Mosleh Hmoud盟——Alsaade Fawaz Waselalla盟——Aldhyani Theyazn h . h . PY - 2021 DA - 2021/04/15 TI -开发集成神经网络模型识别虚假评论的电子商务使用多畴的数据集SP - 5522574六世- 2021 AB -在线产品评论中发挥重要作用的成功或失败的电子商务业务。采购产品或服务之前,消费者通常会通过在线评论发布之前的客户推荐产品的细节和做出购买决定。然而,可以增强或阻碍特定电子商务产品通过发布虚假评论,可以写的人叫骗子。这些评论会导致经济损失的电子商务企业和误导消费者采取了错误的决定,寻找替代产品。因此,开发一个假评估检测系统最终所需的电子商务业务。拟议的方法已经使用四个标准虚假评论的多畴的数据集包括酒店、餐馆、Yelp,亚马逊。进一步预处理方法,如stopword去除,去除标点符号、和标记化表现以及填充序列方法的输入序列在培训、固定长度验证和测试模型。这种方法使用不同大小的数据集,各种输入word-embedding矩阵复习语法功能的文本的帮助下开发和创建word-embedding层是该模型的一个组成部分。CNN卷积和max-pooling层技术实现降维特征提取,分别。基于门机制,LSTM层结合的CNN技术学习和处理的上下文信息审查的文本的语法特征。 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. SN - 1176-2322 UR - https://doi.org/10.1155/2021/5522574 DO - 10.1155/2021/5522574 JF - Applied Bionics and Biomechanics PB - Hindawi KW - ER -