Enhancing deceptive reviewer and review detection in e-commerce: analyzing ensemble Attention-based deep learning models

This publication has 46 references indexed in Scilit:

• SUH-AIFRD: A self-training-based hybrid approach for individual fake reviewer detection
  Multimedia Tools and Applications, 2024
• Vote-based integration of review spam detection algorithms
  Applied Intelligence, 2022
• Deceptive opinion spam detection approaches: a literature survey
  Applied Intelligence, 2022
• Spam review detection using self attention based CNN and bi-directional LSTM
  Multimedia Tools and Applications, 2021
• Spam review detection using LSTM autoencoder: an unsupervised approach
  Electronic Commerce Research, 2020
• Incorporating feature representation into BiLSTM for deceptive review detection
  Computing, 2019
• Opinion spam detection framework using hybrid classification scheme
  Soft Computing, 2019
• Spotting review spammer groups: A cosine pattern and network based method
  Concurrency and Computation: Practice and Experience, 2018
• Graph-based review spammer group detection
  Knowledge and Information Systems, 2017
• Deceptive review detection using labeled and unlabeled data
  Multimedia Tools and Applications, 2016