Fake Review Detection Using Machine Learning Algorithms

Online product reviews have become a foundational component of consumer decision-making across global ecommerce platforms such as Amazon, Flipkart, Zomato, and Google Maps. The rapid proliferation of bot-generated,

incentivized, and adversarially crafted fake reviews has critically eroded the trustworthiness of this feedback ecosystem. Existing

binary classification approaches are inadequate to capture the nuanced and continuous spectrum of review authenticity. This

paper presents Veracity AI, a forensic linguistic web application that evaluates review authenticity through a multi-layered

hybrid architecture combining a custom-built Multinomial Naive Bayes Machine Learning classifier with a Deep Natural

Language Processing pipeline powered by the Google Gemini AI API. The proposed system generates a continuous Trust

Spectrum score (0–100), an Origin Classifier label (e.g., Bot-Generated, Paid Human Writer, Competitor Attack, Genuine), and

a sentence-level autopsy visualization. A Fractional Dataset Weighting mechanism (40% AI weight / 60% user signal) provides

robust defense against adversarial data poisoning attacks. The continuous learning feedback loop stores validated user verdicts

in Firebase Firestore, dynamically retraining the local Naive Bayes model in real time. Experimental evaluation demonstrates

that the Naive Bayes classifier achieves agreement with the NLP ground truth in 83% of test cases, with the system correctly

identifying bot-generated, paid-writer, and genuine review categories across diverse test inputs. The system is deployed as a fullstack React/TypeScript web application, offering an intuitive Bento Grid dashboard with Radar chart visualizations for forensic

linguistic fingerprinting.