Fake reviews, also known as deceptive opinions, are used to mislead people and have gained more importance recently. This is due to the rapid increase in online marketing transactions, such as selling and purchasing. E-commerce provides a facility for customers to post reviews and comment about the product or service when purchased. New customers usually go through the posted reviews or comments on the website before making a purchase decision. However, the current challenge is how new individuals can distinguish truthful reviews from fake ones, which later deceives customers, inflicts losses, and tarnishes the reputation of companies. The present paper attempts to develop an intelligent system that can detect fake reviews on ecommerce platforms using n-grams of the review text and sentiment scores given by the reviewer. The proposed methodology adopted in this study used a standard fake hotel review dataset for experimenting and data preprocessing methods and a term frequency-Inverse document frequency (TF-IDF) approach for extracting features and their representation. For detection and classification, n-grams of review texts were inputted into the constructed models to be classified as fake or truthful. However, the experiments were carried out using four different supervised machine-learning techniques and were trained and tested on a dataset collected from the Trip Advisor website. The classification results of these experiments showed that naïve Bayes (NB), support vector machine (SVM), adaptive boosting (AB), and random forest (RF) received 88%, 93%, 94%, and 95%, respectively, based on testing accuracy and tje F1-score. The obtained results were compared with existing works that used the same dataset, and the proposed methods outperformed the comparable methods in terms of accuracy.

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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.