Publication Date

Spring 2024

Degree Type

Master's Project

Degree Name

Master of Science in Computer Science (MSCS)


Computer Science

First Advisor

Katerina Potika

Second Advisor

Fabio Di Troia

Third Advisor

Genya Ishigaki


Aspect Based Sentiment Analysis, Graph Convolutional Networks


Online reviews significantly influence consumer behavior and business reputa- tions. Detecting fake reviews is important for maintaining trust and integrity in

these platforms. In this project, an application of Aspect-Based Sentiment Analy- sis (ABSA) called FakeDetectionGCN is introduced to distinguish genuine feedback

from deceptive content. The idea is to analyze sentiments related to specific aspects (features) within reviews. Graph Convolutional Networks (GCNs) are used to model the complex contextual dependencies in the review texts. Additionally, SenticNet, an external semantic resource, is integrated to enhance the understanding of sentiments in the reviews. This model is capable of identifying both human-generated as well as computer-generated fake reviews. It has been evaluated on two types of datasets and has shown strong performance across both. Through this project, we contribute to the effective detection of fake reviews and maintaining a trustworthy online review ecosystem.

Available for download on Sunday, May 25, 2025