An Adaptive Model for Catalyzing Digital Marketing Using Machine Learning

صقر, أحمد سامى السيد عطوه; عبدالجابر, سيد; عبية, حنان محمد محمود احمد عبية; محمد عبدالوهاب, أحمد

doi:10.21608/sjrbs.2025.386409.1980

	An Adaptive Model for Catalyzing Digital Marketing Using Machine Learning
المجلة العلمية للبحوث والدراسات التجارية
Volume 39, Issue 3, September 2025, Page 1315-1335 PDF (453.18 K)
Document Type: المقالة الأصلية
DOI: 10.21608/sjrbs.2025.386409.1980
View on SCiNiTO
Authors
أحمد سامى السيد عطوه صقر ¹; سيد عبدالجابر ²; حنان محمد محمود احمد عبية عبية³; أحمد محمد عبدالوهاب ⁴
¹جامعة حلوان
²كلية الحاسبات والذكاء الاصطناعى جامعة حلوان
³كلية التجارة وادارة الاعمال-جامعة حلوان-قسم ادارة الاعمال
⁴نظم المعلومات - كلية التجارة وإدارة الأعمال - جامعة حلوان.
Abstract
In today’s digital era, the vast amount of unstructured data generated daily from different data sources like social media, e-commerce platforms, emails, blogs, and other online sources has made sentiment analysis a critical tool for businesses. This paper aims to analyze Amazon customer reviews using a three-tier approach: (1) data preprocessing, (2) statistical feature selection to identify key variables, and (3) classification with machine learning algorithms—Random Forest, Naïve Bayes, and SVM. The classifiers are evaluated using performance metrics such as accuracy, precision, F-measure, true positive rate, and false negative rate. A comparative analysis reveals their strengths and limitations, providing actionable insights for optimizing sentiment analysis. This paper enhances sentiment analysis through structured processing of unstructured reviews, providing businesses with actionable insights involving stacking ensemble combining Naïve Bayes, Gradient Boosting, Neural Networks under an XGBoost for sentiment analysis to drive data-based decisions and improve customer satisfaction.
Keywords
Sentiment Analysis; Machine Learning; Customer Reviews; Supervised Learning; Digital Marketing


Statistics Article View: 6 PDF Download: 4