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GEmFakeABSA: graph-embedded fake review detection using aspect-based sentiment analysis.

Ganpat Singh Chauhan¹, Atul Kumar Verma², Shikha Chaudhary³

¹Department of Information Technology, Manipal University Jaipur, Jaipur, 303007, India.

Scientific Reports

|June 10, 2026

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This summary is machine-generated.

This study introduces GEmFakeABSA, a novel method for detecting fake reviews using graph-embedded aspect-based sentiment analysis. The model enhances e-commerce by improving the reliability of user reviews for responsible consumption.

Area of Science:

Natural Language Processing
Machine Learning
E-commerce Analytics

Background:

User reviews significantly influence purchasing decisions and product development in e-commerce.
Fake reviews pose a threat to consumers and manufacturers, undermining trust and market transparency.
Reliable review ecosystems are crucial for achieving UN Sustainable Development Goal 12: Responsible Consumption and Production.

Purpose of the Study:

To propose and evaluate GEmFakeABSA, a Graph-Embedded Fake Review Detection system utilizing Aspect-Based Sentiment Analysis (ABSA).
To enhance fake review detection (FRD) by integrating ABSA with Graph Convolutional Networks (GCNs) for improved accuracy and reliability.
To demonstrate the model's effectiveness in identifying unnatural aspect-sentiment patterns indicative of fabricated reviews.

Keywords:

Aspect-based sentiment analysis (ABSA)Dependency graphs Fake review detection (FRD)Graph convolutional networks (GCNs)Linguistic features

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Protocol for Data Collection and Analysis Applied to Automated Facial Expression Analysis Technology and Temporal Analysis for Sensory Evaluation

Published on: August 26, 2016

Main Methods:

Developing a GCN-based ABSA model to capture contextual relationships between words, aspects, and sentiments.
Leveraging dependency graphs to expose inconsistencies in fabricated reviews.
Combining linguistic information, part-of-speech (POS) features, and ABSA-derived embeddings for robust FRD.

Main Results:

GEmFakeABSA achieved 92.4% accuracy on 21,034 Amazon reviews across 30 product categories.
The model surpassed the BiLSTM-ABSA baseline by +2.3% in precision and +2.46% in recall.
Performance improvements were consistent across various features, even when evaluated in isolation.

Conclusions:

The proposed GEmFakeABSA model effectively detects fake reviews by analyzing aspect-based sentiment and contextual relationships.
The GCN integration allows for the encoding of complex user-review-product interactions, improving detection accuracy.
GEmFakeABSA shows significant potential for practical, real-time content moderation in responsible e-commerce environments.