3
views
0
recommends
+1 Recommend
0 collections
    0
    shares
      • Record: found
      • Abstract: found
      • Article: found
      Is Open Access

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

      research-article

      Read this article at

      Bookmark
          There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

          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.

          Related collections

          Most cited references 33

          • Record: found
          • Abstract: not found
          • Article: not found

          Detection of review spam: A survey

            Bookmark
            • Record: found
            • Abstract: not found
            • Article: not found

            Text mining and probabilistic language modeling for online review spam detection

              Bookmark
              • Record: found
              • Abstract: found
              • Article: found
              Is Open Access

              Detection and classification of social media-based extremist affiliations using sentiment analysis techniques

              Identification and classification of extremist-related tweets is a hot issue. Extremist gangs have been involved in using social media sites like Facebook and Twitter for propagating their ideology and recruitment of individuals. This work aims at proposing a terrorism-related content analysis framework with the focus on classifying tweets into extremist and non-extremist classes. Based on user-generated social media posts on Twitter, we develop a tweet classification system using deep learning-based sentiment analysis techniques to classify the tweets as extremist or non-extremist. The experimental results are encouraging and provide a gateway for future researchers.
                Bookmark

                Author and article information

                Contributors
                Journal
                Appl Bionics Biomech
                Appl Bionics Biomech
                ABB
                Applied Bionics and Biomechanics
                Hindawi
                1176-2322
                1754-2103
                2021
                14 April 2021
                : 2021
                Affiliations
                1Department of Computer Science & Information Technology, Dr. Babasaheb Ambedkar Marathwada University, Aurangabad, India
                2Deanship of E-Learning and Distance Education King Faisal University Saudi Arabia, Al-Ahsa, Saudi Arabia
                3Community College of Abqaiq, King Faisal University, P.O. Box 400, Al-Ahsa, Saudi Arabia
                Author notes

                Academic Editor: Fahd Abd Algalil

                Article
                10.1155/2021/5522574
                8062208
                4f051228-a76a-4aef-978c-ddd0b4ce1a05
                Copyright © 2021 Saleh Nagi Alsubari et al.

                This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

                Categories
                Research Article

                Comments

                Comment on this article