Development of Integrated Neural Network Model for Identification of Fake Reviews in E-Commerce Using Multidomain Datasets

Abstract

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.

Cleanup Sketched Drawings: Deep Learning-Based Model

“…(1) The Convolution Layer . Convolutional layer is one of the main components of convolutional neural network used for features extraction [ 42 – 47 ]. The feature maps, or features, like the green and blue squares in Figure 5 , are extracted during the convolution process.…”

Online Troll Reviewer Detection Using Deep Learning Techniques

“…Trolls frequently utilize postings in online discussions to disseminate phony reviews, spam, or connections to malicious websites that carry computer viruses. Different systems, such as troll-bots [ 6 ], can be used to generate spam and poisonous content on the internet. Automatic systems for recognizing and blocking suspicious reviews are being developed by web platforms.…”

Developing an Intelligent System with Deep Learning Algorithms for Sentiment Analysis of E-Commerce Product Reviews

“…The main task of this layer is to avoid the overfitting of the model [ 52 ]. Here, we assigned the value 0.4 to the dropout rate parameter, where this value has a range between 0 and 1.…”

“…LSTM is a type of RNN capable of learning long-term dependence [ 52 ]. We used an LSTM layer and assigned it to 50 hidden units toward the next layer.…”

“…The hierarchical bidirectional RNN is an example of a hybrid neural network [ 51 ]. The main issue with sentiment analysis of product reviews in the e-commerce domain is the existence of fake reviews that lead customers to select undesired products [ 52 ].…”