TL;DR
This paper presents a method to detect fake Amazon reviews by combining semantic analysis with metadata, aiming to identify suspicious reviews through an algorithm tested for accuracy.
Contribution
The paper introduces a novel algorithm that integrates semantic and metadata analysis to effectively identify fake reviews on Amazon.
Findings
Algorithm achieved high accuracy in detecting fake reviews
Semantic and metadata features improve detection effectiveness
Analysis based on six qualities of code supports robustness
Abstract
Often, there are suspicious Amazon reviews that seem to be excessively positive or have been created through a repeating algorithm. I moved to detect fake reviews on Amazon through semantic analysis in conjunction with meta data such as time, word choice, and the user who posted. I first came up with several instances that may indicate a review isn't genuine and constructed what the algorithm would look like. Then I coded the algorithm and tested the accuracy of it using statistical analysis and analyzed it based on the six qualities of code.
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Taxonomy
TopicsMisinformation and Its Impacts · Spam and Phishing Detection