Advancing E-Commerce Authenticity: A Novel Fusion Approach Based on Deep Learning and Aspect Features for Detecting False Reviews-Reference-Cited by-同舟云学术

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Advancing E-Commerce Authenticity: A Novel Fusion Approach Based on Deep Learning and Aspect Features for Detecting False Reviews

Published:2024 Issue: Volume:12 Page:116055-116070
ISSN:2169-3536
Container-title:IEEE Access
language:
Short-container-title:IEEE Access

Author:

Abd-Alhalem Samia M.¹^ORCID,Ali Hesham Arafat¹,Soliman Naglaa F.²^ORCID,Algarni Abeer D.²,Marie Hanaa Salem¹^ORCID

Affiliation:

1. Faculty of Artificial Intelligence, Delta University for Science and Technology, Gamasa, Egypt

2. Department of Information Technology, College of Computer and Information Sciences, Princess Nourah Bint Abdulrahman University, P.O. Box 84428, Riyadh, Saudi Arabia

Funder

Princess Nourah bint Abdulrahman University Researchers Supporting Project number

Princess Nourah bint Abdulrahman University, Riyadh, Saudi Arabia

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Link

http://xplorestaging.ieee.org/ielx8/6287639/10380310/10614578.pdf?arnumber=10614578

Reference50 articles.

1. Research on false review detection Methods: A state-of-the-art review

2. Decomposing the effects of online customer reviews on brand, price, and product attributes

3. Negative online consumer reviews: effects of different responses

4. What Yelp Fake Review Filter Might Be Doing?

5. Collective Opinion Spam Detection

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