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This study introduces a CNN-LSTM model to detect fake e-commerce reviews, achieving high accuracy in both in-domain and cross-domain experiments. The fake review detection system effectively identifies fraudulent content, protecting businesses and consumers.

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Area of Science:

  • Artificial Intelligence
  • Natural Language Processing
  • E-commerce Security

Background:

  • Online reviews significantly influence consumer purchasing decisions and e-commerce business success.
  • Fake reviews pose a threat by misguiding consumers and causing financial losses to businesses.
  • A robust fake review detection system is crucial for maintaining trust and integrity in e-commerce.

Purpose of the Study:

  • To develop and evaluate a novel fake review detection system for e-commerce platforms.
  • To leverage a hybrid Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) model for enhanced fake review identification.
  • To assess the model's performance across multiple domains and in cross-domain scenarios.

Main Methods:

  • Utilized four standard fake review datasets (hotels, restaurants, Yelp, Amazon).
  • Applied preprocessing techniques including stopword removal, punctuation removal, tokenization, and padding.
  • Developed a CNN-LSTM model incorporating word-embedding layers, CNN for feature extraction, and LSTM for contextual information learning.

Main Results:

  • In-domain experiment accuracies: 77% (restaurants), 85% (hotels), 86% (Yelp), 87% (Amazon).
  • Cross-domain experiment achieved an accuracy of 89%.
  • The proposed CNN-LSTM model outperformed existing approaches in in-domain experiments based on accuracy.

Conclusions:

  • The proposed CNN-LSTM model demonstrates strong performance in detecting fake reviews across various e-commerce domains.
  • The hybrid model effectively captures contextual information and n-gram features for accurate classification.
  • This system offers a promising solution for combating fake reviews and enhancing e-commerce reliability.