Research on Multifeature Fusion False Review Detection Based on DynDistilBERT-BiLSTM-CNN-Reference-Cited by-同舟云学术

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Research on Multifeature Fusion False Review Detection Based on DynDistilBERT-BiLSTM-CNN

Published:2024-09-15 Issue:18 Volume:11 Page:30040-30053
ISSN:2327-4662
Container-title:IEEE Internet of Things Journal
language:
Short-container-title:IEEE Internet Things J.

Author:

Zhang Jing¹^ORCID,Huang Ke¹^ORCID,Lang Ding²,Xu Yuguang¹,Li Hong-An¹^ORCID,Li Xuewen³

Affiliation:

1. School of Computer Science and Technology, Xi’an University of Science and Technology, Xi’an, China

2. School of Energy Engineering, Xi’an University of Science and Technology, Xi’an, China

3. School of Safety Science and Engineering, Xi’an University of Science and Technology, Xi’an, China

Funder

National Natural Science Foundation of China

Post Doctoral Research Foundation of China

Key Research and Development Foundation of Shaanxi Province

Scientific Research Project of Shaanxi Provincial Education Department

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Link

http://xplorestaging.ieee.org/ielx8/6488907/10660568/10549979.pdf?arnumber=10549979

Reference35 articles.

1. Impacts of consumer cognitive process to ascertain online fake review: A cognitive dissonance theory approach

2. Building Fake Review Detection Model Based on Sentiment Intensity and PU Learning

3. Fake review detection in e-Commerce platforms using aspect-based sentiment analysis

4. A deep learning approach for detecting fake reviewers: Exploiting reviewing behavior and textual information

5. Fake review detection system for online E-commerce platforms: A supervised general mixed probability approach

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