Paper Title
GuardBuy: An AI-Based Intelligent System for Pre-Purchase Regret Prediction in Online Shopping Using Hybrid Machine Learning
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Registration ID: IJNRD_324640
Published ID: IJNRD2605211
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Keywords
Artificial Intelligence, Machine Learning, Natural Language Processing, Online Shopping, Sentiment Analysis, TF-IDF, Hybrid Model
Abstract
Online shopping has become an essential part of modern life, providing customers with convenient access to a wide variety of products through different e-commerce platforms. Despite these advantages, many customers find themselves dissatisfied with items they have acquired, as those items fail to align with what was initially anticipated. This phenomenon, known as post-purchase regret, has become a common issue in online shopping environments. This paper proposes GuardBuy, an intelligent AI-based system that predicts potential post-purchase dissatisfaction and provides customers with a regret risk assessment before finalizing an online purchase. The system retrieves product reviews from a curated dataset of over 140,000 Amazon product reviews and applies a multi-stage analysis pipeline including text preprocessing, feature extraction via TF-IDF, sentiment analysis using VADER, and a hybrid machine learning model combining Logistic Regression (70%) and Support Vector Machine (30%). A novel BUT-aware preprocessing technique is introduced to identify and prioritize complaint-bearing text segments. Experimental results demonstrate that the Logistic Regression model achieves 91% accuracy with 0.75 recall for the regret class, while the Linear SVM achieves 93% accuracy with 0.60 recall. The hybrid model achieves 93% overall accuracy with improved consistency in regret detection. The system is deployed as a complete end-to-end web application enabling real-time purchase risk assessment. Index Terms—Artificial Intelligence, Machine Learning, Natural Language Processing, Online Shopping, Post-Purchase Regret, Sentiment Analysis, TF-IDF, Hybrid Classification, E-Commerce, Decision Support Systems
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How To Cite (APA)
Sadeem Muslet Almutairi, Maryam Alshammari, Noha Khalid Altalasi, Lama Mutlab Alshammari, & Norah Ayedh Alhajji (May-2026). GuardBuy: An AI-Based Intelligent System for Pre-Purchase Regret Prediction in Online Shopping Using Hybrid Machine Learning. INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT, 11(5), c88-c96. https://ijnrd.org/papers/IJNRD2605211.pdf
Issue
Volume 11 Issue 5, May-2026
Pages : c88-c96
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Paper Reg. ID: IJNRD_324640
Published Paper Id: IJNRD2605211
Research Area: Other area not in list
Author Type: Foreign Author
Country: Hail, Hail, Saudi Arabia
Published Paper PDF: https://ijnrd.org/papers/IJNRD2605211.pdf
Published Paper URL: https://ijnrd.org/viewpaperforall?paper=IJNRD2605211
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