Publication Date
Spring 2024
Degree Type
Master's Project
Degree Name
Master of Science in Computer Science (MSCS)
Department
Computer Science
First Advisor
Katerina Potika
Second Advisor
Fabio Di Troia
Third Advisor
Genya Ishigaki
Keywords
Aspect Based Sentiment Analysis, Graph Convolutional Networks
Abstract
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.
Recommended Citation
Phukon, Prathana, "Detecting Fake Reviews using Aspect Based Sentiment Analysis and Graph Convolutional Networks" (2024). Master's Projects. 1393.
DOI: https://doi.org/10.31979/etd.z2q5-tgqw
https://scholarworks.sjsu.edu/etd_projects/1393
